Research topics include:
 Computational Hemodynamics
 Multiscale modeling of elastic tissues
 Agentbased and hybrid modeling
 Physicsbased data assimilation in elastography
List of recent projects
 Computational multiscale methods for inverse estimation of effective properties of poroelastic tissues (DFG, 20212024). PIs: A. Caiazzo, Daniel Peterseim (University of Augsburg)
 Datadriven reducedorder methods for the noninvasive estimation of blood flow biomarkers from phasecontrast MRI data (DFG RTN DAEDALUS, 20212024). PIs: V. John, W.C. Müller (TU Berlin), A. Caiazzo (associated)
 Mathematical Framework for MR Poroelastography (MATH+, Ef39*, 2021). PIs: A. Caiazzo, K. Tabelow, I. Sack (Charité)
 Quantitative Tissue Pressure Imaging via PDEInformed Assimilation of MR Data (MATH+, EF311, 20222023). PIs: A. Caiazzo, K. Tabelow, I. Sack (Charité)
Computational Hemodynamics
Numerical simulations of blood flows can provide additional insights in the complex processes undergoing in the cardiovascular system, as well as support clinicians and experimentalists in linking observable quantities (biomarkers) with pathological conditions such as stenosis or hypertension.
On the one hand, mathematical models shall be setup using available data obtained via medical imaging, such as arterial geometries and blood flow measurements. On the other hand, it is necessary to employ efficient and robust numerical methods for solving the underlying hemodynamics, which often requires the modeling of turbulent flows or the solution of PDE in complex domains.
Research at WIAS focuses on the efficient and robust solution of NavierStokes equations, as well as the solution of inverse problems relating the dynamics of blood flow and available medical data. Current research topics include:
 Modeling of turbulent blood flow and sensitivity with respect to model parameters
 Reducedorder modeling based on Proper Orthogonal Decomposition
 Inverse problems for the estimation of onedimensional blood flow model parameters
 Numerical methods for estimating flow boundaries and pressure drops from velocity data (Preprint)
S. Katz, A. Caiazzo, B. Moreau, U. Wilbrandt, J. Brüning, L. Goubergrits, V. John, Impact of turbulence modeling on the simulation of blood flow in aortic coarctation, International Journal of Numerical Methods in Biomedical Engineering, 39 (2023), pp. e3695/1e3695/36, DOI 10.1002/cnm.3695.
Modeling of biomaterials and biological tissues
The dynamics of biological tissues involved the interplay of multiple processes happening on a wide range of spatial and temporal scales. Computational (insilico) models can play a relelvant role in understanding the linkage between the available experimental data, which can be often measured at limited resolution and/or limited to particular quantity of interests, and the underlying biological or physical processes.
In this area, the research at WIAS focuses on different approaches for the modeling of tissues, also combining different discretization methods. Of particular relevance are
 Multiscale models for vascularized tissues, with the purpose of studying how changes in the fluid dynamics in a vascular network affects the behavior of the effective tissue
 Numerical methods for multiscale inverse problems, i.e., where data are available only on a macro scale, coarser than the scale needed for describing accurately the tissue dynamics
 Application of reduced order modeling & machine learning, also in combination with numerical methods for computational hemodynamics
 Complex soft and biological materials, focusing on phase field models for complex materials the with applications in the modeling of hydrogels and polyelectrolyte gels, cells, proteins, and tumors. This research is closely related to the main application areas Material Modeling, and in particular the research topic Complex soft and biological materials Phase field models for complex materials and interfaces.
Agentbased modeling
Agentbased models (ABMs, also called IndividualBased models) aim at modeling a complex process considering the interaction of the single entities (agents) involved in it. In the context of biological applications, ABMs seek to model the dynamics at the scale of single cells, prescribing a set of deterministic and stochastic rules that regulate biophysical processes such as cell growth, mitosis, mutation, and biomechanical interaciton between cells.
Due to their capability of including biological mechanisms at the smallest scales, ABMs offer the possibility of realistic simulations, and they are nowadays widely used within many areas of bio mathematical research related to cell population growth. Particularly interesting applications are related to mathematical oncology (cancer growth modeling).
Research at WIAS focuses on the usage of ABMs in the context of multiscale models, i.e., combining them with continuous, PDEbased, models that can more efficiently recover macroscale dynamics. A further topic of research concerns the linkage between the discrete ABMs, in which the stochasticity is modeled at the level of single cells, and stochastic PDEs.
Cardiac cell models
Cardiac arrhythmias are a common health problem and are on the rise due to increasing life expectancy. There are a variety of possible triggers and the consequences can range up to sudden cardiac death. Mathematical modeling and analysis of increasingly better (patientspecific) models can be used to identify critical transitions in cardiac rhythm and individual risk factors. At WIAS, on the one hand, we deal with modeling of the heart using 3D1D models as well as electromechanical coupling. On the other hand, the focus is on efficient numerical solutions and their mathematical analysis, for example using bifurcation theory. Furthermore, selforganizing electrophysiological patterns occurring in the heart, such as spiral waves and fibrillation, are investigated numerically, see, e.g., Erhardt & Solem (2022), Erhardt & Solem (2021) Erhardt, TsanevaAtanasova, Lines, G. T. and Martens, E. A. (2023)
Physicsbased data assimilation and optimization in medical imaging
The scope of physicsbased modeling is to combine available medical data with underlying physics, concerning the processes involved in data acquisition, as well as with biomechanical models, in order to enhance the quality of data and estimate relevant biophysical parameters. Research in this area focuses on image processing, datadriven optimization, learning, and their combination with advanced methods for inumerical simulations.
Of particular importance are problems related to Magnetic Resonance Imaging (MRI), ranging from functional, diffusionweighted, quantitative MRI to image reconstruction in Magnetic Resonance Fingerprinting, inversion recovery MRI, and magnetic resonance elastography (MRE). The methods developed in this project arise from different areas of mathematics, such as nonparametric statistics, nonsmooth variational methods, and reducedorder models for data assimilation.
Assimilation of magnetic resonance elastography data
Magnetic resonance elastography (MRE) is a tissue imaging modality designed to measure mechanical properties of biological tissues. During a MRE examination, the tissue undergoes harmonic mechanical excitation (10?100 Hz) generated by actuators placed on the body surface. By using phasecontrast MRI, the propagation of mechanical waves induced by the external forces, the mechanical response of the tissue is recorded as a threedimensional internal displacement field. These data, combined with physical tissue models and different inversion methods, allow to obtain quantitative information on tissue mechanical properties, e.g., in terms of mechanical parameters.
The clinical potential of elastography has been constantly increasing in the last decades, especially for the quantitative estimation of biomarkers (such as elastic parameters, tissue fluidity, viscoelasticity) related to different tissue pathologies. Focusing on the brain, elastography has been used for the characterization of cancer tissue and for the early stage diagnosis of neurological diseases due to alteration of microstructure properties of brain tissue. Recent clinical research suggested that MRE has the potential to identify also abnormal increases in intracranial pressure, which can be responsible of neurological disorders.
To this purpose, the mathematical models used in MRE needs to account for a fluid component, in order to be able to characterize the tissue pressure. Moreover, one major challenge concerns the availability of the experimental data and their resolution. In fact, MRE data are typically available only on a small portion of the brain (few slices), whilst the intracranial pressure increase shall be characterized at the scale of the whole organ.
Research in this area focuses on data assimilation techniques for state estimation, i.e., computational and mathematical frameworks to reconstruct physical solutions on the whole brain  such as displacement and pressure fields  starting from partially available data. See Galarce et al., 2022 for more details.
Publications
Monographs

A.H. Erhardt, K. TsanevaAtanasova, G.T. Lines, E.A. Martens, eds., Dynamical Systems, PDEs and Networks for Biomedical Applications: Mathematical Modeling, Analysis and Simulations, Special Edition, articles published in Frontiers of Physics, Frontiers in Applied Mathematics and Statistics, and Frontiers in Physiology, Frontiers Media SA, Lausanne, Switzerland, 2023, 207 pages, (Collection Published), DOI 10.3389/9782832514580 .

A. Caiazzo, I.E. VignonClementel, Chapter 3: Mathematical Modeling of Blood Flow in the Cardiovascular System, in: Quantification of Biophysical Parameters in Medical Imaging, I. Sack, T. Schaeffter, eds., Springer International Publishing, Cham, 2018, pp. 4570, (Chapter Published), DOI 10.1007/9783319659244_3 .
Articles in Refereed Journals

A. Erhardt, D. Peschka, Ch. Dazzi, L. Schmeller, A. Petersen, S. Checa, A. Münch, B. Wagner, Modeling cellular selforganization in strainstiffening hydrogels, Computational Mechanics, published online on 31.08.2024, DOI 10.1007/s00466024025367 .
Abstract
We develop a threedimensional mathematical model framework for the collective evolution of cell populations by an agentbased model (ABM) that mechanically interacts with the surrounding extracellular matrix (ECM) modeled as a hydrogel. We derive effective twodimensional models for the geometrical setup of a thin hydrogel sheet to study cellcell and cellhydrogel mechanical interactions for a range of external conditions and intrinsic material properties. We show that without any stretching of the hydrogel sheets, cells show the wellknown tendency to form long chains with varying orientations. Our results further show that external stretching of the sheet produces the expected nonlinear strainsoftening or stiffening response, with, however, little qualitative variation of the overall cell dynamics for all the materials considered. The behavior is remarkably different when solvent is entering or leaving from strain softening or stiffening hydrogels, respectively. 
S. Katz, A. Caiazzo, V. John, Impact of viscosity modeling on the simulation of aortic blood flow, Journal of Computational and Applied Mathematics, 425 (2023), pp. 115036/1115036/18, DOI 10.1016/j.cam.2022.115036 .
Abstract
Modeling issues for the simulation of blood flow in an aortic coarctation are studied in this paper. From the physical point of view, several viscosity models for nonNewtonian fluids as well as a Newtonian fluid model will be considered. From the numerical point of view, two different turbulence models are utilized in the simulations. The impact of both, the physical and the numerical modeling, on clinically relevant biomarkers is investigated and compared. 
S. Katz, A. Caiazzo, B. Moreau, U. Wilbrandt, J. Brüning, L. Goubergrits, V. John, Impact of turbulence modeling on the simulation of blood flow in aortic coarctation, International Journal of Numerical Methods in Biomedical Engineering, 39 (2023), pp. e3695/1e3695/36, DOI 10.1002/cnm.3695 .
Abstract
Numerical simulations of pulsatile blood flow in an aortic coarctation require the use of turbulence modeling. This paper considers three models from the class of large eddy simulation (LES) models (Smagorinsky, Vreman, model) and one model from the class of variational multiscale models (residualbased) within a finite element framework. The influence of these models on the estimation of clinically relevant biomarkers used to assess the degree of severity of the pathological condition (pressure difference, secondary flow degree, normalized flow displacement, wall shear stress) is investigated in detail. The simulations show that most methods are consistent in terms of severity indicators such as pressure difference and stenotic velocity. Moreover, using secondorder velocity finite elements, different turbulence models might lead to considerably different results concerning other clinically relevant quantities such as wall shear stresses. These differences may be attributed to differences in numerical dissipation introduced by the turbulence models. 
A. Alphonse, D. Caetano, A. Djurdjevac, Ch.M. Elliot, Function spaces, time derivatives and compactness for evolving families of Banach spaces with applications to PDEs, Journal of Differential Equations, 353 (2023), pp. 268338, DOI 10.1016/j.jde.2022.12.032 .
Abstract
We develop a functional framework suitable for the treatment of partial differential equations and variational problems on evolving families of Banach spaces. We propose a definition for the weak time derivative that does not rely on the availability of a Hilbertian structure and explore conditions under which spaces of weakly differentiable functions (with values in an evolving Banach space) relate to classical Sobolev?Bochner spaces. An Aubin?Lions compactness result is proved. We analyse concrete examples of function spaces over timeevolving spatial domains and hypersurfaces for which we explicitly provide the definition of the time derivative and verify isomorphism properties with the aforementioned Sobolev?Bochner spaces. We conclude with the proof of well posedness for a class of nonlinear monotone problems on an abstract evolving space (generalising the evolutionary pLaplace equation on a moving domain or surface) and identify some additional problems that can be formulated with the setting developed in this work. 
A.C. Moore, M.G. Hennessy, L.P. Nogueira, S.J. Franks, M. Taffetani, H. Seong , Y.K. Kang, W.S. Tan, G. Miklosic, R. El Laham, K. Zhou, L. Zharova, J.R. King , B. Wagner, H.J. Haugen, A. Münch, M.M. Stevens, Fiber reinforced hydrated networks recapitulate the poroelastic mechanics of articular cartilage, Acta Biomaterialia, 167 (2023), pp. 6982, DOI 10.1016/j.actbio.2023.06.015 .

N. Kornilov, A. Gasnikov, P. Dvurechensky, D. Dvinskikh, Gradient free methods for nonsmooth convex stochastic optimization with heavytailed noise on convex compact, Computational Management Science, 20 (2023), pp. 37/137/43, DOI 10.1007/s10287023004702 .

M. Bongarti, L.D. Galvan, L. Hatcher, M.R. Lindstrom, Ch. Parkinson, Ch. Wang, A.L. Bertozzi , Alternative SIAR models for infectious diseases and applications in the study of noncompliance, Mathematical Models & Methods in Applied Sciences, 32 (2022), pp. 19872015, DOI 10.1142/S0218202522500464 .
Abstract
In this paper, we use modified versions of the SIAR model for epidemics to propose two ways of understanding and quantifying the effect of noncompliance to nonpharmaceutical intervention measures on the spread of an infectious disease. The SIAR model distinguishes between symptomatic infected (I) and asymptomatic infected (A) populations. One modification, which is simpler, assumes a known proportion of the population does not comply with government mandates such as quarantining and socialdistancing. In a more sophisticated approach, the modified model treats noncompliant behavior as a social contagion. We theoretically explore different scenarios such as the occurrence of multiple waves of infections. Local and asymptotic analyses for both models are also provided. 
P. Colli, G. Gilardi, J. Sprekels, Wellposedness for a class of phasefield systems modeling prostate cancer growth with fractional operators and general nonlinearities, Atti della Accademia Nazionale dei Lincei. Rendiconti Lincei. Matematica e Applicazioni, 33 (2022), pp. 193228, DOI 10.4171/rlm/969 .
Abstract
This paper deals with a general system of equations and conditions arising from a mathematical model of prostate cancer growth with chemotherapy and antiangiogenic therapy that has been recently introduced and analyzed (see [P. Colli et al., Mathematical analysis and simulation study of a phasefield model of prostate cancer growth with chemotherapy and antiangiogenic therapy effects, Math. Models Methods Appl. Sci. bf 30 (2020), 12531295]). The related system includes two evolutionary operator equations involving fractional powers of selfadjoint, nonnegative, unbounded linear operators having compact resolvents. Both equations contain nonlinearities and in particular the equation describing the dynamics of the tumor phase variable has the structure of a AllenCahn equation with doublewell potential and additional nonlinearity depending also on the other variable, which represents the nutrient concentration. The equation for the nutrient concentration is nonlinear as well, with a term coupling both variables. For this system we design an existence, uniqueness and continuous dependence theory by setting up a careful analysis which allows the consideration of nonsmooth potentials and the treatment of continuous nonlinearities with general growth properties. 
P. Colli, A. Signori, J. Sprekels, Optimal control problems with sparsity for tumor growth models involving variational inequalities, Journal of Optimization Theory and Applications, 194 (2022), pp. 2558, DOI 10.1007/s10957022020007 .
Abstract
This paper treats a distributed optimal control problem for a tumor growth model of CahnHilliard type including chemotaxis. The evolution of the tumor fraction is governed by a variational inequality corresponding to a double obstacle nonlinearity occurring in the associated potential. In addition, the control and state variables are nonlinearly coupled and, furthermore, the cost functional contains a nondifferentiable term like the $L^1$norm in order to include sparsity effects which is of utmost relevance, especially time sparsity, in the context of cancer therapies as applying a control to the system reflects in exposing the patient to an intensive medical treatment. To cope with the difficulties originating from the variational inequality in the state system, we employ the socalled “deep quench approximation” in which the convex part of the double obstacle potential is approximated by logarithmic functions. For such functions, firstorder necessary conditions of optimality can be established by invoking recent results. We use these results to derive corresponding optimality conditions also for the double obstacle case, by deducing a variational inequality in terms of the associated adjoint state variables. The resulting variational inequality can be exploited to also obtain sparsity results for the optimal controls. 
G. Dong, M. Hintermüller, K. Papafitsoros, Optimization with learninginformed differential equation constraints and its applications, ESAIM. Control, Optimisation and Calculus of Variations, 28 (2022), pp. 3/13/44, DOI 10.1051/cocv/2021100 .
Abstract
Inspired by applications in optimal control of semilinear elliptic partial differential equations and physicsintegrated imaging, differential equation constrained optimization problems with constituents that are only accessible through datadriven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machinelearned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided. 
P. Krejčí, E. Rocca, J. Sprekels, Analysis of a tumor model as a multicomponent deformable porous medium, Interfaces and Free Boundaries. Mathematical Modelling, Analysis and Computation, 24 (2022), pp. 235262, DOI 10.4171/IFB/472 .
Abstract
We propose a diffuse interface model to describe tumor as a multicomponent deformable porous medium. We include mechanical effects in the model by coupling the mass balance equations for the tumor species and the nutrient dynamics to a mechanical equilibrium equation with phasedependent elasticity coefficients. The resulting PDE system couples two CahnHilliard type equations for the tumor phase and the healthy phase with a PDE linking the evolution of the interstitial fluid to the pressure of the system, a reactiondiffusion type equation for the nutrient proportion, and a quasistatic momentum balance. We prove here that the corresponding initialboundary value problem has a solution in appropriate function spaces. 
G. Shanmugasundaram, G. Arumugam, A.H. Erhardt, N. Nagarajan, Global existence of solutions to a twospecies predatorprey parabolic chemotaxis system, International Journal of Biomathematics, 15 (2022), pp. 2250054/12250054/23, DOI 10.1142/S1793524522500541 .

L. Lilaj, H. Harthum, T. Meyer, M. Shahrayari, G. Bertalan, A. Caiazzo, J. Braun, Th. Fischer, S. Hirsch, I. Sack, Inversionrecovery MR elastography of the human brain for improved stiffness quantification near fluidsolid boundaries, Magnetic Resonance in Medicine, (2021), published online on 28.06.2021, DOI 10.1002/mrm.28898 .

P. Colli, A. Signori, J. Sprekels, Secondorder analysis of an optimal control problem in a phase field tumor growth model with singular potentials and chemotaxis, ESAIM. Control, Optimisation and Calculus of Variations, 27 (2021), pp. 73/173/46, DOI 10.1051/cocv/2021072 .
Abstract
This paper concerns a distributed optimal control problem for a tumor growth model of CahnHilliard type including chemotaxis with possibly singular anpotentials, where the control and state variables are nonlinearly coupled. First, we discuss the weak wellposedness of the system under very general assumptions for the potentials, which may be singular and nonsmooth. Then, we establish the strong wellposedness of the system in a reduced setting, which however admits the logarithmic potential: this analysis will lay the foundation for the study of the corresponding optimal control problem. Concerning the optimization problem, we address the existence of minimizers and establish both firstorder necessary and secondorder sufficient conditions for optimality. The mathematically challenging secondorder analysis is completely performed here, after showing that the solution mapping is twice continuously differentiable between suitable Banach spaces via the implicit function theorem. Then, we completely identify the secondorder Fréchet derivative of the controltostate operator and carry out a thorough and detailed investigation about the related properties. 
L. Heltai, A. Caiazzo, L.O. Müller, Multiscale coupling of onedimensional vascular models and elastic tissues, Annals of Biomedical Engineering (ABME), published online on 20.07.2021, DOI 10.1007/s10439021028040 .
Abstract
We present a computational multiscale model for the efficient simulation of vascularized tissues, composed of an elastic threedimensional matrix and a vascular network. The effect of blood vessel pressure on the elastic tissue is surrogated via hypersingular forcing terms in the elasticity equations, which depend on the fluid pressure. In turn, the blood flow in vessels is treated as a onedimensional network. The pressure and velocity of the blood in the vessels are simulated using a highorder finite volume scheme, while the elasticity equations for the tissue are solved using a finite element method. This work addresses the feasibility and the potential of the proposed coupled multiscale model. In particular, we assess whether the multiscale model is able to reproduce the tissue response at the effective scale (of the order of millimeters) while modeling the vasculature at the microscale. We validate the multiscale method against a full scale (threedimensional) model, where the fluid/tissue interface is fully discretized and treated as a Neumann boundary for the elasticity equation. Next, we present simulation results obtained with the proposed approach in a realistic scenario, demonstrating that the method can robustly and efficiently handle the oneway coupling between complex fluid microstructures and the elastic matrix. 
J. Sprekels, F. Tröltzsch, Sparse optimal control of a phase field system with singular potentials arising in the modeling of tumor growth, ESAIM. Control, Optimisation and Calculus of Variations, 27 (2021), pp. S26/1S26/27, DOI 10.1051/cocv/2020088 .
Abstract
In this paper, we study an optimal control problem for a nonlinear system of reactiondiffusion equations that constitutes a simplified and relaxed version of a thermodynamically consistent phase field model for tumor growth originally introduced in [13]. The model takes the effect of chemotaxis into account but neglects velocity contributions. The unknown quantities of the governing state equations are the chemical potential, the (normalized) tumor fraction, and the nutrient extracellular water concentration. The equation governing the evolution of the tumor fraction is dominated by the variational derivative of a doublewell potential which may be of singular (e.g., logarithmic) type. In contrast to the recent paper [10] on the same system, we consider in this paper sparsity effects, which means that the cost functional contains a nondifferentiable (but convex) contribution like the L^{1}norm. For such problems, we derive firstorder necessary optimality conditions and conditions for directional sparsity, both with respect to space and time, where the latter case is of particular interest for practical medical applications in which the control variables are given by the administration of cytotoxic drugs or by the supply of nutrients. In addition to these results, we prove that the corresponding controltostate operator is twice continuously differentiable between suitable Banach spaces, using the implicit function theorem. This result, which complements and sharpens a differentiability result derived in [10], constitutes a prerequisite for a future derivation of secondorder sufficient optimality conditions. 
P. Colli, G. Gilardi, J. Sprekels, Asymptotic analysis of a tumor growth model with fractional operators, Asymptotic Analysis, 120 (2020), pp. 4172, DOI 10.3233/ASY191578 .
Abstract
In this paper, we study a system of three evolutionary operator equations involving fractional powers of selfadjoint, monotone, unbounded, linear operators having compact resolvents. This system constitutes a generalized and relaxed version of a phase field system of CahnHilliard type modelling tumor growth that has originally been proposed in HawkinsDaarud et al. (Int. J. Numer. Math. Biomed. Eng. 28 (2012), 324). The original phase field system and certain relaxed versions thereof have been studied in recent papers coauthored by the present authors and E. Rocca. The model consists of a CahnHilliard equation for the tumor cell fraction φ, coupled to a reactiondiffusion equation for a function S representing the nutrientrich extracellular water volume fraction. Effects due to fluid motion are neglected. Motivated by the possibility that the diffusional regimes governing the evolution of the different constituents of the model may be of different (e.g., fractional) type, the present authors studied in a recent note a generalization of the systems investigated in the abovementioned works. Under rather general assumptions, wellposedness and regularity results have been shown. In particular, by writing the equation governing the evolution of the chemical potential in the form of a general variational inequality, also singular or nonsmooth contributions of logarithmic or of double obstacle type to the energy density could be admitted. In this note, we perform an asymptotic analysis of the governing system as two (small) relaxation parameters approach zero separately and simultaneously. Corresponding wellposedness and regularity results are established for the respective cases; in particular, we give a detailed discussion which assumptions on the admissible nonlinearities have to be postulated in each of the occurring cases. 
B. Franchi, M. Heida, S. Lorenzani, A mathematical model for Alzheimer's disease: An approach via stochastic homogenization of the Smoluchowski equation, Communications in Mathematical Sciences, 18 (2020), pp. 11051134, DOI 10.4310/CMS.2020.v18.n4.a10 .
Abstract
In this note, we apply the theory of stochastic homogenization to find the asymptotic behavior of the solution of a set of Smoluchowski's coagulationdiffusion equations with nonhomogeneous Neumann boundary conditions. This system is meant to model the aggregation and diffusion of βamyloid peptide (Aβ) in the cerebral tissue, a process associated with the development of Alzheimer's disease. In contrast to the approach used in our previous works, in the present paper we account for the nonperiodicity of the cellular structure of the brain by assuming a stochastic model for the spatial distribution of neurons. Further, we consider nonperiodic random diffusion coefficients for the amyloid aggregates and a random production of Aβ in the monomeric form at the level of neuronal membranes. 
C.K. Macnamara, A. Caiazzo, I. RamisConde, M.A.J. Chaplain, Computational modelling and simulation of cancer growth and migration within a 3D heterogeneous tissue: The effects of fibre and vascular structure, Journal of Computational Science, 40 (2020), pp. 101067/1101067/24, DOI 10.1016/j.jocs.2019.101067 .
Abstract
The term cancer covers a multitude of bodily diseases, broadly categorised by having cells which do not behave normally. Since cancer cells can arise from any type of cell in the body, cancers can grow in or around any tissue or organ making the disease highly complex. Our research is focused on understanding the specific mechanisms that occur in the tumour microenvironment via mathematical and computational modeling. We present a 3D individualbased model which allows one to simulate the behaviour of, and spatiotemporal interactions between, cells, extracellular matrix fibres and blood vessels. Each agent (a single cell, for example) is fully realised within the model and interactions are primarily governed by mechanical forces between elements. However, as well as the mechanical interactions we also consider chemical interactions, for example, by coupling the code to a finite element solver to model the diffusion of oxygen from blood vessels to cells. The current state of the art of the model allows us to simulate tumour growth around an arbitrary bloodvessel network or along the striations of fibrous tissue. 
G. Dong, H. Guo, Parametric polynomial preserving recovery on manifolds, SIAM Journal on Scientific Computing, 42 (2020), pp. A1885A1912, DOI 10.1137/18M1191336 .

P. Colli, G. Gilardi, J. Sprekels, A distributed control problem for a fractional tumor growth model, Mathematics  Open Access Journal, 7 (2019), pp. 792/1792/32, DOI 10.3390/math7090792 .
Abstract
In this paper, we study the distributed optimal control of a system of three evolutionary equations involving fractional powers of three selfadjoint, monotone, unbounded linear operators having compact resolvents. The system is a generalization of a CahnHilliard type phase field system modeling tumor growth that goes back to HawkinsDaarud et al. (Int. J. Numer. Math. Biomed. Eng. 28 (2012), 324.) The aim of the control process, which could be realized by either administering a drug or monitoring the nutrition, is to keep the tumor cell fraction under control while avoiding possible harm for the patient. In contrast to previous studies, in which the occurring unbounded operators governing the diffusional regimes were all given by the Laplacian with zero Neumann boundary conditions, the operators may in our case be different; more generally, we consider systems with fractional powers of the type that were studied in the recent work Adv. Math. Sci. Appl. 28 (2019), 343375 by the present authors. In our analysis, we show the Fréchet differentiability of the associated controltostate operator, establish the existence of solutions to the associated adjoint system, and derive the firstorder necessary conditions of optimality for a cost functional of tracking type. 
P. Colli, G. Gilardi, J. Sprekels, Wellposedness and regularity for a fractional tumor growth model, Advances in Mathematical Sciences and Applications, 28 (2019), pp. 343375.
Abstract
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P. Colli, A. Signori, J. Sprekels, Optimal control of a phase field system modelling tumor growth with chemotaxis and singular potentials, Applied Mathematics and Optimization. An International Journal with Applications to Stochastics, 83 (2021), pp. 20172049 (published online on 21.10.2019), and 2021 Correction to: Optimal control of a phase field system modelling tumor growth with chemotaxis and singular potentials (https://doi.org/10.1007/s0024502109771x), DOI 10.1007/s00245019096186 .
Abstract
A distributed optimal control problem for an extended model of phase field type for tumor growth is addressed. In this model, the chemotaxis effects are also taken into account. The control is realized by two control variables that design the dispensation of some drugs to the patient. The cost functional is of tracking type, whereas the potential setting has been kept quite general in order to allow regular and singular potentials to be considered. In this direction, some relaxation terms have been introduced in the system. We show the wellposedness of the state system, the Fréchet differentiability of the controltostate operator in a suitable functional analytic framework, and, lastly, we characterize the firstorder necessary conditions of optimality in terms of a variational inequality involving the adjoint variables. 
G. Dong, M. Hintermüller, K. Papafitsoros, Quantitative magnetic resonance imaging: From fingerprinting to integrated physicsbased models, SIAM Journal on Imaging Sciences, 2 (2019), pp. 927971, DOI 10.1137/18M1222211 .
Abstract
Quantitative magnetic resonance imaging (qMRI) is concerned with estimating (in physical units) values of magnetic and tissue parameters, e.g., relaxation times $T_1$, $T_2$, or proton density $rho$. Recently, in [Ma et al., Nature, 495 (2013), pp. 187193], magnetic resonance fingerprinting (MRF) was introduced as a technique being capable of simultaneously recovering such quantitative parameters by using a twostep procedure: (i) given a probe, a series of magnetization maps are computed and then (ii) matched to (quantitative) parameters with the help of a precomputed dictionary which is related to the Bloch manifold. In this paper, we first put MRF and its variants into perspective with optimization and inverse problems to gain mathematical insights concerning identifiability of parameters under noise and interpretation in terms of optimizers. Motivated by the fact that the Bloch manifold is nonconvex and that the accuracy of the MRFtype algorithms is limited by the ?discretization size? of the dictionary, a novel physicsbased method for qMRI is proposed. In contrast to the conventional twostep method, our model is dictionaryfree and is rather governed by a single nonlinear equation, which is studied analytically. This nonlinear equation is efficiently solved via robustified Newtontype methods. The effectiveness of the new method for noisy and undersampled data is shown both analytically and via extensive numerical examples, for which improvement over MRF and its variants is also documented. 
S.P. Frigeri, C.G. Gal, M. Grasselli, J. Sprekels, Strong solutions to nonlocal 2D CahnHilliardNavierStokes systems with nonconstant viscosity, degenerate mobility and singular potential, Nonlinearity, 32 (2019), pp. 678727, DOI 10.1088/13616544/aaedd0 .
Abstract
We consider a nonlinear system which consists of the incompressible NavierStokes equations coupled with a convective nonlocal CahnHilliard equation. This is a diffuse interface model which describes the motion of an incompressible isothermal mixture of two (partially) immiscible fluids having the same density. We suppose that the viscosity depends smoothly on the order parameter as well as the mobility. Moreover, we assume that the mobility is degenerate at the pure phases and that the potential is singular (e.g. of logarithmic type). This system is endowed with noslip boundary condition for the (average) velocity and homogeneous Neumann boundary condition for the chemical potential. Thus the total mass is conserved. In the twodimensional case, this problem was already analyzed in some joint papers of the first three authors. However, in the present general case, only the existence of a global weak solution, the (conditional) weakstrong uniqueness and the existence of the global attractor were proven. Here we are able to establish the existence of a (unique) strong solution through an approximation procedure based on time discretization. As a consequence, we can prove suitable uniform estimates which allow us to show some smoothness of the global attractor. Finally, we discuss the existence of strong solutions for the convective nonlocal CahnHilliard equation, with a given velocity field, in the three dimensional case as well. 
L. Heltai, A. Caiazzo, Multiscale modeling of vascularized tissues via nonmatching immersed methods, International Journal of Numerical Methods in Biomedical Engineering, 35 (2019), pp. 3264/13264/32, DOI 10.1002/cnm.3264 .
Abstract
We consider a multiscale approach based on immersed methods for the efficient computational modeling of tissues composed of an elastic matrix (in two or threedimensions) and a thin vascular structure (treated as a codimension two manifold) at a given pressure. We derive different variational formulations of the coupled problem, in which the effect of the vasculature can be surrogated in the elasticity equations via singular or hypersingular forcing terms. These terms only depends on information defined on codimension two manifolds (such as vessel center line, cross sectional area, and mean pressure over cross section), thus drastically reducing the complexity of the computational model. We perform several numerical tests, ranging from simple cases with known exact solutions to the modeling of materials with random distributions of vessels. In the latter case, we use our immersed method to perform an in silico characterization of the mechanical properties of the effective biphasic material tissue via statistical simulations. 
L.O. Müller, A. Caiazzo, P.J. Blanco, Reducedorder unscented Kalman filter with observations in the frequency domain: Application to computational hemodynamics, IEEE Transactions on Biomedical Engineering, 66 (2019), pp. 12691276, DOI 10.1109/TBME.2018.2872323 .
Abstract
Objective: The aim of this work is to assess the potential of the reduced order unscented Kalman filter (ROUKF) in the context of computational hemodynamics, in order to estimate cardiovascular model parameters when employing real patientspecific data. Methods: The approach combines an efficient blood flow solver for onedimensional networks (for the forward problem) with the parameter estimation problem cast in the frequency space. Namely, the ROUKF is used to correct model parameter after each cardiac cycle, depending on the discrepancies of model outputs with respect to available observations properly mapped into the frequency space. Results: First we validate the filter in frequency domain applying it in the context of a set of experimental measurements for an in vitro model. Second, we perform different numerical experiments aiming at parameter estimation using patientspecific data. Conclusion: Our results demonstrate that the filter in frequency domain allows a faster and more robust parameter estimation, when compared to its time domain counterpart. Moreover, the proposed approach allows to estimate parameters that are not directly related to the network but are crucial for targeting interindividual parameter variability (e.g., parameters that characterize the cardiac output). Significance: The ROUKF in frequency domain provides a robust and flexible tool for estimating parameters related to cardiovascular mathematical models using in vivo data. 
J. Sprekels, H. Wu, Optimal distributed control of a CahnHilliardDarcy system with mass sources, Applied Mathematics and Optimization. An International Journal with Applications to Stochastics, 83 (2021), pp. 489530 (published online on 24.01.2019), DOI 10.1007/s00245019095554 .
Abstract
In this paper, we study an optimal control problem for a twodimensional CahnHilliardDarcy system with mass sources that arises in the modeling of tumor growth. The aim is to monitor the tumor fraction in a finite time interval in such a way that both the tumor fraction, measured in terms of a tracking type cost functional, is kept under control and minimal harm is inflicted to the patient by administering the control, which could either be a drug or nutrition. We first prove that the optimal control problem admits a solution. Then we show that the controltostate operator is Fréchet differentiable between suitable Banach spaces and derive the firstorder necessary optimality conditions in terms of the adjoint variables and the usual variational inequality. 
C. Bertoglio, A. Caiazzo, Y. Bazilevs, M. Braack, M. EsmailyMoghadam, V. Gravemeier, A.L. Marsden, O. Pironneau, I.E. VignonClementel, W.A. Wall, Benchmark problems for numerical treatment of backflow at open boundaries, International Journal of Numerical Methods in Biomedical Engineering, 34 (2018), pp. e2918/1e2918/34, DOI 10.1002/cnm.2918 .
Abstract
In computational fluid dynamics, incoming velocity at open boundaries, or backflow, often yields to unphysical instabilities already for moderate Reynolds numbers. Several treatments to overcome these backflow instabilities have been proposed in the literature. However, these approaches have not yet been compared in detail in terms of accuracy in different physiological regimes, in particular due to the difficulty to generate stable reference solutions apart from analytical forms. In this work, we present a set of benchmark problems in order to compare different methods in different backflow regimes (with a full reversal flow and with propagating vortices after a stenosis). The examples are implemented in FreeFem++ and the source code is openly available, making them a solid basis for future method developments. 
L. Blank, A. Caiazzo, F. Chouly, A. Lozinski, J. Mura, Analysis of a stabilized penaltyfree Nitsche method for the Brinkman, Stokes, and Darcy problems, ESAIM: Mathematical Modelling and Numerical Analysis, 52 (2018), pp. 21492185, DOI 10.1051/m2an/2018063 .

A. Caiazzo, F. Caforio, G. Montecinos, L.O. Müller, P.J. Blanco, E.F. Toro, Assessment of reduced order Kalman filter for parameter identification in onedimensional blood flow models using experimental data, International Journal of Numerical Methods in Biomedical Engineering, 33 (2017), pp. e2843/1e2843/26, DOI 10.1002/cnm.2843 .
Abstract
This work presents a detailed investigation of a parameter estimation approach based on the reduced order unscented Kalman filter (ROUKF) in the context of onedimensional blood flow models. In particular, the main aims of this study are (i) to investigate the effect of using real measurements vs. synthetic data (i.e., numerical results of the same in silico model, perturbed with white noise) for the estimation and (ii) to identify potential difficulties and limitations of the approach in clinically realistic applications in order to assess the applicability of the filter to such setups. For these purposes, our numerical study is based on the in vitro model of the arterial network described by [Alastruey et al. 2011, J. Biomech. bf 44], for which experimental flow and pressure measurements are available at few selected locations. In order to mimic clinically relevant situations, we focus on the estimation of terminal resistances and arterial wall parameters related to vessel mechanics (Young's modulus and thickness) using few experimental observations (at most a single pressure or flow measurement per vessel). In all cases, we first perform a theoretical identifiability analysis based on the generalized sensitivity function, comparing then the results obtained with the ROUKF, using either synthetic or experimental data, to results obtained using reference parameters and to available measurements. 
C. Bertoglio, A. Caiazzo, A Stokesresidual backflow stabilization method applied to physiological flows, Journal of Computational Physics, 313 (2016), pp. 260278.
Abstract
In computational fluid dynamics incoming flow at open boundaries, or emphbackflow, often yields to unphysical instabilities for high Reynolds numbers. It is widely accepted that this is due to the incoming energy arising from the convection term, which cannot be empha priori controlled when the velocity field is unknown at the boundary. In order to improve the robustness of the numerical simulations, we propose a stabilized formulation based on a penalization of the residual of a weak Stokes problem on the open boundary, whose viscous part controls the incoming convective energy, while the inertial term contributes to the kinetic energy. We also present different strategies for the approximation of the boundary pressure gradient, which is needed for defining the stabilization term. The method has the advantage that it does not require neither artificial modifications or extensions of the computational domain. Moreover, it is consistent with the Womersley solution. We illustrate our approach on numerical examples  both academic and reallife  relevant to blood and respiratory flows. The results also show that the stabilization parameter can be reduced with the mesh size. 
A. Caiazzo, R. Guibert, I.E. VignonClementel, A reducedorder modeling for efficient design study of artificial valve in enlarged ventricular outflow tracts, Computer Methods in Biomechanics and Biomedical Engineering, 19 (2016), pp. 13141318.
Abstract
A computational approach is proposed for efficient design study of a reducer stent to be percutaneously implanted in enlarged right ventricular outflow tracts (RVOT). The need for such a device is driven by the absence of bovine or artificial valves which could be implanted in these RVOT to replace the absent or incompetent native valve, as is often the case over time after Tetralogy of Fallot repair. Hemodynamics are simulated in the stented RVOT via a reduce order model based on proper orthogonal decomposition (POD), while the artificial valve is modeled as a thin resistive surface. The reduced order model is obtained from the numerical solution on a reference device configuration, then varying the geometrical parameters (diameter) for design purposes. To validate the approach, forces exerted on the valve and on the reducer are monitored, varying with geometrical parameters, and compared with the results of full CFD simulations. Such an approach could also be useful for uncertainty quantification. 
A. Caiazzo, R. Guibert, Y. Boudjemline, I.E. VignonClementel, Efficient blood flow simulations for the design of stented valve reducer in enlarged ventricular outflow tracts, Cardiovascular Engineering and Technology, 6 (2015), pp. 485500.
Abstract
Tetralogy of Fallot is a congenital heart disease characterized over time, after the initial repair, by the absence of a functioning pulmonary valve, which causes regurgitation, and by progressive enlargement of the right ventricle and pulmonary arteries. Due to this pathological anatomy, available transcatheter valves are usually too small to be deployed in the enlarged right ventricular outflow tracts (RVOT). To avoid surgical valve replacement, an alternative consists in implanting a reducer prior to or in combination with a transcatheter valve. We describe a computational model to study the effect of a stented valve RVOT reducer on the hemodynamics in enlarged ventricular outflow tracts. To this aim, blood flow in the right ventricular outflow tract is modeled via the incompressible NavierStokes equations coupled to a simplified valve model, numerically solved with a standard finite element method and with a reduced order model based on Proper Orthogonal Decomposition (POD). Numerical simulations are based on a patient geometry obtained from medical imaging and boundary conditions tuned according to measurements of inlet flow rates and pressures. Different geometrical models of the reducer are built, varying its length and/or diameter, and compared with the initial devicefree state. Simulations thus investigate multiple device configurations and describe the effect of geometry on hemodynamics. Forces exerted on the valve and on the reducer are monitored, varying with geometrical parameters. Results support the thesis that the reducer does not introduce significant pressure gradients, as was found in animal experiments. Finally, we demonstrate how computational complexity can be reduced with POD. 
A. Caiazzo, G. Montecinos, L.O. Müller, E.M. Haacke, E.F. Toro, Computational haemodynamics in stenotic internal jugular veins, Journal of Mathematical Biology, 70 (2015), pp. 745772.
Abstract
Stenosis in internal jugular veins (IJVs) are frequently associated to pathological venous circulation and insufficient cerebral blood drainage. In this work, we set up a computational framework to assess the relevance of IJV stenoses through numerical simulation, combining medical imaging, patientspecific data and a mathematical model for venous occlusions. Coupling a threedimensional (3D) description of blood flow in IJVs with a reduced onedimesional model (1D) for major intracranial veins, we are able to model different anatomical configurations, an aspect of importance to understand the impact of IJV stenosis in intracranial venous haemodynamics. We investigate several stenotic configurations in a physiologic patientspecific regime, quantifying the effect of the stenosis in terms of venous pressure increase and wall shear stress patterns. Simulation results are in qualitative agreement with reported pressure anomalies in pathological cases. Moreover, they demonstrate the potential of the proposed multiscale framework for individualbased studies and computeraided diagnosis. 
A. Caiazzo, I. RamisConde, Multiscale modeling of palisade formation in glioblastoma multiforme, Journal of Theoretical Biology, 383 (2015), pp. 145156.
Abstract
Palisades are characteristic tissue aberrations that arise in glioblastomas. Observation of palisades is considered as a clinical indicator of the transition from a noninvasive to an invasive tumour. In this article we propose a computational model to study the influence of genotypic and phenotypic heterogeneity in palisade formation. For this we produced three dimensional realistic simulations, based on a multiscale hybrid model, coupling the evolution of tumour cells and the oxygen diffusion in tissue, that depict the shape of palisades during its formation. Our results can be summarized as the following: (1) we show that cell heterogeneity is a crucial factor in palisade formation and tumour growth; (2) we present results that can explain the observed fact that recursive tumours are more malignant than primary tumours; and (3) the presented simulations can provide to clinicians and biologists for a better understanding of palisades 3D structure as well as glioblastomas growth dynamics 
C. Bertoglio, A. Caiazzo, A tangential regularization method for backflow stabilization in hemodynamics, Journal of Computational Physics, 261 (2014), pp. 162171.
Abstract
In computational simulations of fluid flows, instabilities at the Neumann boundaries may appear during backflow regime. It is widely accepted that this is due to the incoming energy at the boundary, coming from the convection term, which cannot be controlled when the velocity field is unknown. We propose a stabilized formulation based on a local regularization of the fluid velocity along the tangential directions on the Neumann boundaries. The stabilization term is proportional to the amount of backflow, and does not require any further assumption on the velocity profile. The perfomance of the method is assessed on a two and threedimensional Womersley flows, as well as considering a hemodynamic physiological regime in a patientspecific aortic geometry. 
A. Caiazzo, J. Mura, Multiscale modeling of weakly compressible elastic materials in harmonic regime and application to microscale structure estimation, Multiscale Modeling & Simulation. A SIAM Interdisciplinary Journal, 12 (2014), pp. 514537.
Abstract
This article is devoted to the modeling of elastic materials composed by an incompressible elastic matrix and small compressible gaseous inclusions, under a time harmonic excitation. In a biomedical context, this model describes the dynamics of a biological tissue (e.g. lung or liver) when wave analysis methods (such as Magnetic Resonance Elastography) are used to estimate tissue properties. Due to the multiscale nature of the problem, direct numerical simulations are prohibitive. We extend the homogenized model introduced in [Baffico, Grandmont, Maday, Osses, SIAM J. Mult. Mod. Sim., 7(1), 2008] to a time harmonic regime to describe the solidgas mixture from a macroscopic point of view in terms of an effective elasticity tensor. Furthermore, we derive and validate numerically analytical approximations for the effective elastic coefficients in terms of macroscopic parameters. This simplified description is used to to set up an efficient variational approach for the estimation of the tissue porosity, using the mechanical response to external harmonic excitations. 
TH.I. Seidman, O. Klein, Periodic solutions of isotone hybrid systems, Discrete and Continuous Dynamical Systems. Series B. A Journal Bridging Mathematics and Sciences, 18 (2013), pp. 483493.
Abstract
Suggested by conversations in 1991 (Mark Krasnosel'skiĭ and Aleksei Pokrovskiĭ with TIS), this paper generalizes earlier work (Krasnosel'skiĭPokrovskiĭ 1974) of theirs by defining a setting of hybrid systems with isotone switching rules for a partially ordered set of modes and then obtaining a periodicity result in that context. An application is given to a partial differential equation modeling calcium release and diffusion in cardiac cells. 
M. Grote, V. Palumberi, B. Wagner, A. Barbero, I. Martin, Dynamic formation of oriented patches in chondrocyte cell cultures, Journal of Mathematical Biology, 63 (2011), pp. 757777.
Abstract
Growth factors have a significant impact not only on the growth dynamics but also on the phenotype of chondrocytes (Barbero et al. , J. Cell. Phys. 204, pp. 830838, 2005). In particular, as chondrocyte populations approach confluence, the cells tend to align and form coherent patches. Starting from a mathematical model for fibroblast populations at equilibrium (Mogilner et al., Physica D 89, pp. 346367, 1996), a dynamic continuum model with logistic growth is developed. Both linear stability analysis and numerical solutions of the timedependent nonlinear integropartial differential equation are used to identify the key parameters that lead to pattern formation in the model. The numerical results are compared quantitatively to experimental data by extracting statistical information on orientation, density and patch size through Gabor filters. 
A. Barbero, V. Palumberi, B. Wagner, R. Sader, M. Grote, I. Martin, Experimental and mathematical study of the influence of growth factors and the kinetics of adult human articular chondrocytes, Journal of Cellular Physiology, 204 (2005), pp. 830838.
Preprints, Reports, Technical Reports

G. Dong, M. Hintermüller, C. Sirotenko, Dictionary learning based regularization in quantitative MRI: A nested alternating optimization framework, Preprint no. 3135, WIAS, Berlin, 2024, DOI 10.20347/WIAS.PREPRINT.3135 .
Abstract, PDF (5706 kByte)
In this article we propose a novel regularization method for a class of nonlinear inverse problems that is inspired by an application in quantitative magnetic resonance imaging (MRI). It is a special instance of a general dynamical image reconstruction problem with an underlying time discrete physical model. Our regularization strategy is based on dictionary learning, a method that has been proven to be effective in classical MRI. To address the resulting nonconvex and nonsmooth optimization problem, we alternate between updating the physical parameters of interest via a LevenbergMarquardt approach and performing several iterations of a dictionary learning algorithm. This process falls under the category of nested alternating optimization schemes. We develop a general such algorithmic framework, integrated with the LevenbergMarquardt method, of which the convergence theory is not directly available from the literature. Global sublinear and local strong linear convergence in infinite dimensions under certain regularity conditions for the subdifferentials are investigated based on the Kurdyka?Lojasiewicz inequality. Eventually, numerical experiments demonstrate the practical potential and unresolved challenges of the method. 
C. Cárcamo, A. Caiazzo, F. Galarce, J. Mura, A stabilized total pressureformulation of the Biot's poroelasticity equations in frequency domain: Numerical analysis and applications, Preprint no. 3101, WIAS, Berlin, 2024, DOI 10.20347/WIAS.PREPRINT.3101 .
Abstract, PDF (7379 kByte)
This work focuses on the numerical solution of the dynamics of a poroelastic material in the frequency domain. We provide a detailed stability analysis based on the application of the Fredholm alternative in the continuous case, considering a total pressure formulation of the Biot's equations. In the discrete setting, we propose a stabilized equal order finite element method complemented by an additional pressure stabilization to enhance the robustness of the numerical scheme with respect to the fluid permeability. Utilizing the Fredholm alternative, we extend the wellposedness results to the discrete setting, obtaining theoretical optimal convergence for the case of linear finite elements. We present different numerical experiments to validate the proposed method. First, we consider model problems with known analytic solutions in two and three dimensions. As next, we show that the method is robust for a wide range of permeabilities, including the case of discontinuous coefficients. Lastly, we show the application for the simulation of brain elastography on a realistic brain geometry obtained from medical imaging. 
CH. Keller, J. Fuhrmann, M. Landstorfer, B. Wagner, A model framework for ion channels with selectivity filters based on continuum nonequilibrium thermodynamics, Preprint no. 3072, WIAS, Berlin, 2023, DOI 10.20347/WIAS.PREPRINT.3072 .
Abstract, PDF (7287 kByte)
A mathematical model framework to describe ion transport in nanopores is presented. The model is based on nonequilibrium thermodynamics and considers finite size effects, solvation phenomena as well as the electrical charges of membrane surfaces and channel proteins. Par ticular emphasis is placed on the consistent modelling of the selectivity filter in the pore. It is treated as an embedded domain in which the constituents can change their chemical properties. The diffusion process through the filter is governed by an independent diffusion coefficient and at the interfaces, de and resolvation reactions are introduced as Neumann interface conditions. The evolution of the molar densities is described by driftdiffusion equations, where the fluxes depend on the gradient of the chemical potentials and the electric force. The chemical potentials depend on the molar fractions and on the pressure in the electrolyte and accounts for solvation effects. The framework allows the calculation of currentvoltage relations for a variety of chan nel properties and ion concentrations. We compare our model framework to experimental results for calciumselective ion channels and show the general validity of our approach. Our parameter studies show that calcium and sodium currents are proportional to the surface charge in the se lectivity filter and to the diffusion coefficients of the ions. Moreover, they show that the negative charges inside the pore have a decisive influence on the selectivity of divalent over monovalent ions.
Talks, Poster

S. Katz, Turbulence modeling in aortic blood flow: traditional models and perspectives on machine learning, VPH (Virtual Physiological Human) Conference 2024, September 4  6, 2024, Universität Stuttgart, September 4, 2024.

A. Caiazzo, Datadriven reducedorder modeling and data assimilation for the characterization of aortic coarctation, 8th International Conference on Computational and Mathematical Biomedical Engineering (CMBE24), June 24  26, 2024, George Mason University, Arlington, Virginia, USA, June 24, 2024.

A. Caiazzo, Multiscale FSI for the effective modeling of vascular tissues, Virtual Physiological Human, VPH Conference 2024, September 4  6, 2024, Universität Stuttgart, September 4, 2024.

A. Caiazzo, Multiscale FSI for the effective modeling of vascular tissues, VPH (Virtual Physiological Human) Conference 2024, September 4  6, 2024, Universität Stuttgart, September 4, 2024.

C. Cárcamo, Frequencydomain formulation and convergence analysis of Biot's poroelasticity equations based on total pressure, Computational Techniques and Applications Conference (CTAC 2024), November 19  22, 2024, Monash University, School of Mathematics, Melbourne, Australia, November 20, 2024.

C. Cárcamo, Frequencydomain formulation and convergence analysis of Biots poroelasticity equations based on total pressure, The Chemnitz Finite Element Symposium 2024, September 9  11, 2024, Technische Universität Chemnitz, September 9, 2024.

V. John, On two modeling issues in aortic blood flow simulations, Seminar of Dr. Nagaiah Chamakuri, Scientific Computing Group (SCG), School of Mathematics, Indian Institute of Science Education and Research, Thiruvananthapuram, Kerala, India, February 20, 2024.

F. Romor, Efficient numerical resolution of parametric partial differential equations on solution manifolds parametrized by neural networks, 9th European Congress on Computational Methods in Applied Sciences and Engineering, June 3  7, 2024, ECCOMAS, scientific organization, Lissabon, Portugal, June 4, 2024.

F. Romor, Registrationbased data assimilation of aortic blood flow, Leibniz MMS Days 2024, Kaiserslautern, April 10  12, 2024.

F. Romor, Registrationbased data assimilation of aortic blood flow, Leibniz MMS Days 2024, April 10  12, 2024, Leibniz Network "Mathematical Modeling and Simulation", Leibniz Institut für Verbundwerkstoffe GmbH (IVW), Kaiserslautern.

B. Wagner, On disordered regions driving phase separation of proteins under variable salt concentration, Cellular Matters Conference 2023, Ascona, Switzerland, June 4  7, 2023.

B. Wagner, On disordered regions driving phase separation of proteins under variable salt concentration, Cellular Matters Conference 2023, June 4  7, 2023, Ascona, Switzerland.

C. Cárcamo Sanchez, F. Galarce Marín, A. Caiazzo, I. Sack, K. Tabelow, Quantitative tissue pressure imaging via PDEinformed assimilation of MRdata, MATH+ Day, HumboldtUniversität zu Berlin, October 20, 2023.

J. Fuhrmann, Ch. Keller, M. Landstorfer, B. Wagner, Development of an ionchannel modelframework for invitro assisted interpretation of current voltage relations, MATH+ Day, HumboldtUniversität zu Berlin, October 20, 2023.

CH. Keller, J. Fuhrmann, M. Landstorfer, B. Wagner, Development of an ionchannel modelframework for invitro assisted interpretation of current voltage relations, MATH+Day 2022, Technische Universität Berlin, November 18, 2022.

M. Bongarti, Boundary stabilization of nonlinear dynamics of acoustics waves under the JMGT equation (online talk), Early Career Math Colloquium, University of Arizona, Tucson, USA, October 12, 2022.

J. Sprekels, Deep quench approach and sparsity in the optimal control of a phase field model for tumor growth, PHAse field MEthods in applied sciences (PHAME 2022), May 23  27, 2022, Istituto Nazionale di Alta Matematica, Rome, Italy, May 27, 2022.

C. Sirotenko, Dictionary learning for an in inverse problem in quantitative MRI, 92th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2022), Session 21 ``Mathematical Signal and Image Processing'', August 15  19, 2022, RheinischWestfälische Technische Hochschule Aachen, August 16, 2022.

C. Sirotenko, Dictionary learning for an inverse problem in quantitative MRI (online talk), SIAM Conference on Imaging Science (IS22) (Online Event), Minisymposium ``Recent Advances of Inverse Problems in Imaging'', March 21  25, 2022, March 25, 2022.

A.H. Erhardt, S. Checa, A. Petersen, B. Wagner, AA112: Mathematical modelling of cellular selforganization on stimuli responsive extracellular matrix (online poster), MATH+ Day 2021 (Online Event), Technische Universität Berlin, November 5, 2021.

A. Caiazzo, F. Galarce Marín, J. Polzehl, I. Sack, K. Tabelow, Physics based assimilation of displacements data from magnetic resonance elastography, Kickoff Workshop of the MATH+ Thematic Einstein Semester on Mathematics of Imaging in RealWorld Challenges (Hybrid Event), Berlin, October 6  8, 2021.

M. Kantner, Mathematical modeling and optimal control of the COVID19 pandemic (online talk), Mathematisches Kolloquium, Bergische Universität Wuppertal, April 27, 2021.

B. Wagner, Phasefield models of the lithiation/delithiation cycle of thinfilm electrodes (online talk), Oxford Battery Modelling Symposium (Online Event), March 16  17, 2020, University of Oxford, UK, March 16, 2020.

J.A. Brüggemann, Elliptic obstacletype quasivariational inequalities (QVIs) with volume constraints motivated by a contact problem in biomedicine, ICCOPT 2019  Sixth International Conference on Continuous Optimization, Berlin, August 5  8, 2019.

J.A. Brüggemann, Solution methods for a class of obstacletype quasi variational inequalities with volume constraints, ICCOPT 2019  Sixth International Conference on Continuous Optimization, Session ``QuasiVariational Inequalities and Generalized Nash Equilibrium Problems (Part II)'', August 5  8, 2019, Berlin, August 7, 2019.

A. Münch, B. Wagner, Nonlinear viscoelastic effects of polymer and hydrogel layers sliding on liquid substrates, 694. WEHeraeusSeminar, Bad Honnef, April 11  13, 2019.

B. Wagner, S. Reber, J. Iglesias, A. Fritsch, E. Meca, Hierarchical spindle assembly: Sequencedependent energy landscapes for a cytoplasmic condensate, KickOff Meeting DFG SPP 2191 ``Molecular Mechanisms of Functional Phase Separation'', Heidelberg, June 6  7, 2019.

B. Wagner, Free boundary problems of active and driven hydrogels, PIMSGermany Workshop on Modelling, Analysis and Numerical Analysis of PDEs for Applications, June 24  26, 2019, Universität Heidelberg, Interdisciplinary Center for Scientific Computing and BIOQUANT Center, June 24, 2019.

B. Wagner, Free boundary problems of active and driven hydrogels, EUROMECH 604, Fluid and Solid Mechanics for Issue Engineering, September 23  25, 2019, University of Oxford, Mathematical Institute, UK, September 24, 2019.

A. Caiazzo, Data assimilation in onedimensional hemodynamics, European Conference on Numerical Mathematics and Advanced Applications (ENUMATH 2019), Minisymposium 36 ``DataDriven Computational Fluid Dynamics (Part 2)'', September 30  October 4, 2019, Eindhoven University of Technology, Netherlands, October 1, 2019.

A. Caiazzo, Multiscale hybrid modeling and simulation of cancer growth within a 3D heterogeneous tissue, CanadaGermany Workshop Mathematical Biology and Numerics, June 24  26, 2019, Universität Heidelberg, June 26, 2019.

K. Papafitsoros, Generating structure nonsmooth priors for image reconstruction, Young Researchers in Imaging Seminars, March 20  27, 2019, Henri Poincaré Institute, Paris, France, March 27, 2019.

K. Papafitsoros, Generating structure nonsmooth priors for image reconstruction, ICCOPT 2019  Sixth International Conference on Continuous Optimization, August 5  8, 2019, Berlin, August 6, 2019.

J.A. Brüggemann, Pathfollowing methods for a class of elliptic obstacletype quasivariational problems with integral constraints, 23rd International Symposium on Mathematical Programming (ISMP2018), Session 370 ``Variational Analysis 4'', July 1  6, 2018, Bordeaux, France, July 2, 2018.

B. Wagner, Thin film models for an active gel, 89th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2018), Session S02 ``Biomechanics'', March 19  23, 2018, Technische Universität München, March 22, 2018.

A. Caiazzo, Mathematical modeling and simulations of geothermal reservoirs, Virtual Physiological Human Conference (VPH2018), September 5  7, 2018, University of Zaragoza, Spain, September 6, 2018.

A. Caiazzo, Robust open boundary conditions and efficient data assimilation in multiscale hemodynamics, International Symposium ``Modeling, Simulation and Optimization of the Cardiovascular System'', October 22  24, 2018, Universität Magdeburg, October 22, 2018.

A. Caiazzo, Towards the personalization of (1D) bloodflow simulations, University of Amsterdam, Computational Science Lab, Netherlands, September 21, 2018.

M. Hintermüller, M. Holler, K. Papafitsoros, A function space framework for structural total variation regularization in inverse problems, MIA 2018  Mathematics and Image Analysis, HumboldtUniversität zu Berlin, January 15  17, 2018.

K. Papafitsoros, A function space framework for structural total variation regularization with applications in inverse problems, VI Latin American Workshop on Optimization and Control (LAWOC 18), September 3  7, 2018, Quito, Ecuador, September 4, 2018.

A. Caiazzo, Estimation of cardiovascular system parameters from real data, 2nd Leibniz MMS Days 2017, February 22  23, 2017, Technische Informationsbibliothek, Hannover, February 22, 2017.

A. Caiazzo, Homogenization methods for weakly compressible elastic materials forward and inverse problem, Workshop on Numerical Inverse and Stochastic Homogenization, February 13  17, 2017, Universität Bonn, Hausdorff Research Institute for Mathematics, February 17, 2017.

A. Caiazzo, A comparative study of backflow stabilization methods, 7th European Congress of Mathematics (7ECM), July 18  22, 2016, Technische Universität Berlin, Berlin, July 19, 2016.

A. Caiazzo, Backflow stabilization methods for open boundaries, ChristianAlbrechtsUniversität zu Kiel, Angewandte Mathematik, Kiel, May 19, 2016.

A. Caiazzo, Multiscale modeling of weakly compressible elastic materials in harmonic regime, Rheinische FriedrichWilhelmsUniversität Bonn, Institut für Numerische Simulation, Bonn, May 21, 2015.
External Preprints

N. Kornilov, A. Gasnikov, P. Dvurechensky, D. Dvinskikh, Gradient free methods for nonsmooth convex stochastic optimization with heavytailed noise on convex compact, Preprint no. arXiv:2304.02442, Cornell University, 2023, DOI 10.48550/arXiv.2304.02442 .

D.A. Pasechnyuk, M. Persiianov, P. Dvurechensky, A. Gasnikov, Algorithms for Euclideanregularised optimal transport, Preprint no. arXiv:2307.00321, Cornell University, 2023, DOI 10.48550/arXiv.2307.00321 .
Mathematical Context
 Analysis of Partial Differential Equations and Evolutionary Equations
 Numerical Methods for PDEs with Stochastic Data
 Numerical methods for problems from fluid dynamics
 Optimal control of partial differential equations and nonlinear optimization
 Systems of partial differential equations: modeling, numerical analysis and simulation
 Variational methods