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Beiträge zu Sammelwerken 2022

  • C. Belponer, A. Caiazzo, L. Heltai, L.O. Müller, D. Peterseim, Multiscale and homogenized modeling of vascular tissues, in: 7th International Conference on Computational & Mathematical Biomedical Engineering (CMBE22), 27th -- 29th June, 2022, Milan, Italy, P. Nithiarasu, C. Vergara, eds., 1, CMBE, Cardiff, UK, 2022, pp. 29--31.
  • A. Beznosikov, P. Dvurechensky, A. Koloskova, V. Samokhin, S.U. Stich, A. Gasnikov, Decentralized local stochastic extra-gradient for variational inequalities, in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), S. Kojeyo, S. Mohamed, A. Argawal, D. Belgrave, K. Cho, A. Oh, eds., 2022, pp. 38116--38133.
  • S. Chezhegov, A. Novitskii, A. Rogozin, S. Parsegov, P. Dvurechensky, A. Gasnikov, A general framework for distributed partitioned optimization, 9th IFAC Conference on Networked Systems NECSYS 2022, Zürich, Switzerland, July 5 - 7, 2022, 55 of IFAC-PapersOnLine, Elsevier, 2022, pp. 139--144, DOI 10.1016/j.ifacol.2022.07.249 .
  • A. Gasnikov, A. Novitskii, V. Novitskii, F. Abdukhakimov, D. Kamzolov, A. Beznosikov, M. Takáč, P. Dvurechensky, B. Gu, The power of first-order smooth optimization for black-box non-smooth problems, in: Proceedings of the 39th International Conference on Machine Learning, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, S. Sabato, eds., 162 of Proceedings of Machine Learning Research, 2022, pp. 7241--7265.
  • E. Gorbunov, M. Danilova, D. Dobre, P. Dvurechensky, A. Gasnikov, G. Gidel, Clipped stochastic methods for variational inequalities with heavy-tailed noise, in: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, eds., 2022, pp. 31319--31332.
  • D. Yarmoshik, A. Rogozin, O.O. Khamisov, P. Dvurechensky, A. Gasnikov, Decentralized convex optimization under affine constraints for power systems control, in: Mathematical Optimization Theory and Operations Research. MOTOR 2022, P. Pardalos, M. Khachay, V. Mazalov, eds., 13367 of Lecture Notes in Computer Science, Springer, Cham, 2022, pp. 62--75, DOI 10.1007/978-3-031-09607-5_5 .
  • M. Kirstein, D. Sommer, M. Eigel, Tensor-train kernel learning for Gaussian processes, in: Proceedings of the Eleventh Symposium on Conformal and Probabilistic Prediction with Applications, U. Johansson, H. Boström, K.A. Nguyen, Z. Luo, L. Carlsson, eds., 179 of Proceedings of Machine Learning Research, 2022, pp. 253--272.
  • G. Thiele, Th. Johanni, D. Sommer, M. Eigel, J. Krüger, OptTopo: Automated set-point optimization for coupled systems using topology information, in: 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, 2022, pp. 224--229, DOI 10.1109/CoDIT55151.2022.9803985 .
  • M. O'Donovan, P. Farrell, T. Streckenbach, Th. Koprucki, S. Schulz, Carrier transport in (In,Ga)N quantum well systems: Connecting atomistic tight-binding electronic structure theory to drift-diffusion simulations, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 97--98, DOI 10.1109/NUSOD54938.2022.9894745 .
  • D. Abdel, N. Courtier, P. Farrell, Volume exclusion effects in perovskite charge transport modeling, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2022), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 107--108, DOI 10.1109/NUSOD54938.2022.9894826 .
  • J. Moatti, P. Farrell, Comparison of flux discretizations for varying band-edge energies, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2022), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 103--104, DOI 10.1109/NUSOD54938.2022.9894742 .
  • S. Piani, W. Lei, L. Heltai, N. Rotundo, P. Farrell, Data-driven doping reconstruction, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2022), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 109--110, DOI 10.1109/NUSOD54938.2022.9894774 .
  • C. Geiersbach, E. Loayza-Romero, K. Welker, PDE-constrained shape optimization: Towards product shape spaces and stochastic models, in: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging -- Mathematical Imaging and Vision, K. Chen, C.-B. Schönlieb, X.-Ch. Tai, L. Younces, eds., Springer International Publishing AG, Cham, pp. 1585--1630, DOI 10.1007/978-3-030-98661-2_120 .
  • D. Gahururu, M. Hintermüller, S.-M. Stengl, Th.M. Surowiec, Generalized Nash equilibrium problems with partial differential operators: Theory, algorithms and risk aversion, in: Non-Smooth and Complementarity-Based Distributed Parameter Systems: Simulation and Hierarchical Optimization, M. Hintermüller, R. Herzog, Ch. Kanzow, M. Ulbrich, S. Ulbrich, eds., 172 of International Series of Numerical Mathematics, Birkhäuser, Springer Nature Switzerland AG, Cham, 2022, pp. 145--181.
  • A. Alphonse, M. Hintermüller, C.N. Rautenberg, Stability and sensitivity analysis for quasi-variational inequalities, in: Non-Smooth and Complementarity-Based Distributed Parameter Systems: Simulation and Hierarchical Optimization, M. Hintermüller, R. Herzog, Ch. Kanzow, M. Ulbrich, S. Ulbrich, eds., 172 of International Series of Numerical Mathematics, Birkhäuser, Springer Nature Switzerland AG, Cham, 2022, pp. 183--210.
  • C. Grässle, M. Hintermüller, M. Hinze, T. Keil, Simulation and control of a nonsmooth Cahn--Hilliard Navier--Stokes system with variable fluid densities, in: Non-Smooth and Complementarity-Based Distributed Parameter Systems: Simulation and Hierarchical Optimization, M. Hintermüller, R. Herzog, Ch. Kanzow, M. Ulbrich, S. Ulbrich, eds., 172 of International Series of Numerical Mathematics, Birkhäuser, Springer Nature Switzerland AG, Cham, 2022, pp. 211--240.
  • Z. Benomar, Ch. Ghribi, E. Cali, A. Hinsen, B. Jahnel, Agent-based modeling and simulation for malware spreading in D2D networks, AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, May 11 - 13, 2022, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2022, pp. 91--99.
  • CH. Ghribi, E. Cali, Ch. Hirsch, B. Jahnel, Agent-based simulations for coverage extensions in 5G networks and beyond, in: 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN), M.F. Zhani, N. Limam, P. Borylo, A. Boubendir, C.R.P. Dos Santos, eds., IEEE, 2022, pp. 1--7, DOI 10.1109/ICIN53892.2022.9758136 .
  • M. Kantner, L. Mertenskötter, Data-driven modeling of non-Markovian noise in semiconductor lasers, in: 22nd International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 57--58, DOI 10.1109/NUSOD54938.2022.9894788 .
  • O. Marquardt, M. O'Donovan, S. Schulz, O. Brandt, Th. Koprucki, Influence of random alloy fluctuations on the electronic properties of axial (In,Ga)N/GaN nanowire heterostructures, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 117--118, DOI 10.1109/NUSOD54938.2022.9894777 .
  • Y. Hadjimichael, O. Marquardt, Ch. Merdon, P. Farrell, Band structures in highly strained 3D nanowires, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2022), Turin, Italy, 2022, J. Piprek, Bardella Paolo, eds., IEEE, 2022, pp. 119--120, DOI 10.1109/NUSOD54938.2022.9894837 .
  • M. Radziunas, Steady states in dynamical semiconductor laser models and their analysis, in: 2022 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), Turin, Italy, 2022, J. Piprek, P. Bardella, eds., IEEE, 2022, pp. 49--50, DOI 10.1109/NUSOD54938.2022.9894775 .
  • F. Galarce Marín, K. Tabelow, J. Polzehl, Ch. Panagiotis, V. Vavourakis, I. Sack, A. Caiazzo, Assimilation of magnetic resonance elastography displacement data in brain tissues, in: 7th International Conference on Computational & Mathematical Biomedical Engineering (CMBE22), 27th -- 29th June, 2022, Milan, Italy, P. Nithiarasu, C. Vergara, eds., 2, CMBE, Cardiff, UK, 2022, pp. 648--651.
  • S. Bartels, M. Milicevic, M. Thomas, S. Tornquist, N. Weber, Approximation schemes for materials with discontinuities, in: Non-standard Discretisation Methods in Solid Mechanics, J. Schröder, P. Wriggers, eds., 98 of Lecture Notes in Applied and Computational Mechanics, Springer, Cham, 2022, pp. 505--565, DOI 10.1007/978-3-030-92672-4_17 .
  • M. Thomas, M. Heida, GENERIC for dissipative solids with bulk-interface interaction, in: Research in the Mathematics of Materials Science, M.I. Espanõl, M. Lewicka, L. Scardia, A. Schlömkemper, eds., 31 of Association for Women in Mathematics Series, Springer, Cham, 2022, pp. 333--364, DOI 10.1007/978-3-031-04496-0_15 .
  • A.V. Kovalev, K.M. Grigorenko, S. Slepneva, N. Rebrova, A.G. Vladimirov, G. Huyet, E.A. Viktorov, Bifurcation bridges in mode-locked frequency-swept feedback lasers, in: 2022 International Conference Laser Optics (ICLO), Saint Petersburg, Russian Federation, 2022, IEEE, 2022, pp. 1-1, DOI 10.1109/ICLO54117.2022.9839784 .
  • Y. Nemmour, H. Kremer, B. Schölkopf, J.-J. Zhu, Maximum mean discrepancy distributionally robust nonlinear chance-constrained optimization with finite-sample guarantee, in: 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 2022, pp. 5660--5667, DOI 10.1109/CDC51059.2022.9993212 .
  • D. Agudelo-España, Y. Nemmour, B. Schölkopf, J.-J. Zhu, Learning random feature dynamics for uncertainty quantification, in: 2022 IEEE 61th Conference on Decision and Control (CDC), Cancun, Mexico, IEEE, 2022, pp. 4937--4944, DOI 10.1109/CDC51059.2022.9993152 .
  • J.-J. Zhu, Ch. Kouridi, Y. Nemmour, B. Schölkopf, Adversarially robust kernel smoothing, in: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, G. Camps-Valls, F.J.R. Ruiz, I. Valera, eds., 151 of Proceedings of Machine Learning Research, 2022, pp. 4972--4994.
  • H. Kremer, J.-J. Zhu, K. Muandet, B. Schölkopf, Functional generalized empirical likelihood estimation for conditional moment restrictions, in: Proceedings of the 39th International Conference on Machine Learning, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, S. Sabato, eds., 162 of Proceedings of Machine Learning Research, 2022, pp. 11665--11682.
  • S.H.K. Narayanan, Th. Propson, M. Bongarti, J. Hückelheim, P. Hovland, Reducing memory requirements of quantum optimal control, in: ICCS 2022: Computational Science -- ICCS 2022, D. Groen, C. DE Mulatier, M. Paszynski, V.V. Krzhizhanovskaya, J.J. Dongarra, P.M.A. Sloot, eds., 13353 of Lecture Notes in Computer Science, Springer, Cham, 2022, pp. 129--142, DOI 10.1007/978-3-031-08760-8_11 .
  • J.C. De Los Reyes, D. Villacís, Bilevel optimization methods in imaging, in: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, K. Chen, C.-B. Schönlieb, X.-Ch. Tai, L. Younces, eds., Springer, Cham, pp. published online on 17.02.2022, DOI 10.1007/978-3-030-03009-4_66-1 .