Publications
Monographs

M. Hintermüller, R. Herzog, Ch. Kanzow, M. Ulbrich, S. Ulbrich, eds., NonSmooth and ComplementarityBased Distributed Parameter Systems: Simulation and Hierarchical Optimization, 172 of International Series of Numerical Mathematics, Birkhäuser, Springer Nature Switzerland AG, Cham, 2022, viii + 519 pages, (Collection Published), DOI 10.1007/9783030793937 .
Articles in Refereed Journals

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. 
M. Hintermüller, A. Kröner, Differentiability properties for boundary control of fluidstructure interactions of linear elasticity with NavierStokes equations with mixedboundary conditions in a channel, Applied Mathematics and Optimization. An International Journal with Applications to Stochastics, 87 (2023), pp. 15/115/38, DOI 10.1007/s00245022099380 .
Abstract
In this paper we consider a fluidstructure interaction problem given by the steady Navier Stokes equations coupled with linear elasticity taken from [Lasiecka, Szulc, and Zochoswki, Nonl. Anal.: Real World Appl., 44, 2018]. An elastic body surrounded by a liquid in a rectangular domain is deformed by the flow which can be controlled by the Dirichlet boundary condition at the inlet. On the walls along the channel homogeneous Dirichlet boundary conditions and on the outflow boundary donothing conditions are prescribed. We recall existence results for the nonlinear system from that reference and analyze the control to state mapping generaziling the results of [Wollner and Wick, J. Math. Fluid Mech., 21, 2019] to the setting of the nonlinear NavierStokes equation for the fluid and the situation of mixed boundary conditions in a domain with corners. 
M. Bongarti, I. Lasiecka, J.H. Rodrigues, Boundary stabilization of the linear MGT equation with partially absorbing boundary data and degenerate viscoelasticity, Discrete and Continuous Dynamical Systems  Series S, 15 (2022), pp. 13551376, DOI 10.3934/dcdss.2022020 .

M. Bongarti, I. Lasiecka, Boundary feedback stabilization of a critical nonlinear JMGT equation with Neumannundissipated part of the boundary, Discrete and Continuous Dynamical Systems  Series S, 15 (2022), pp. 19571985, DOI 10.3934/dcdss.2022107 .

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, published online in Nov. 2022, 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. 
J.C. De Los Reyes, K. Herrera, Parameter space study of optimal scaledependent weights in TV image denoising, Applicable Analysis. An International Journal, published online on 03.02.2022, DOI 10.1080/00036811.2022.2033231 .

M. Brokate, M. Ulbrich, Newton differentiability of convex functions in normed spaces and of a class of operators, SIAM Journal on Optimization, 32 (2022), pp. 12651287, DOI 10.1137/21M1449531 .

C. Geiersbach, M. Hintermüller, Optimality conditions and MoreauYosida regularization for almost sure state constraints, ESAIM. Control, Optimisation and Calculus of Variations, 28 (2022), 36, DOI 10.1051/cocv/2022070 .
Abstract
We analyze a potentially riskaverse convex stochastic optimization problem, where the control is deterministic and the state is a Banachvalued essentially bounded random variable. We obtain strong forms of necessary and sufficient optimality conditions for problems subject to equality and conical constraints. We propose a MoreauYosida regularization for the conical constraint and show consistency of the optimality conditions for the regularized problem as the regularization parameter is taken to infinity. 
A. Alphonse, C.N. Rautenberg, J.F. Rodrigues, Analysis of a quasivariational contact problem arising in thermoelasticity, Nonlinear Analysis. An International Mathematical Journal, 217 (2022), pp. 112728/1112728/40, DOI 10.1016/j.na.2021.112728 .
Abstract
We formulate and study two mathematical models of a thermoforming process involving a membrane and a mould as implicit obstacle problems. In particular, the membranemould coupling is determined by the thermal displacement of the mould that depends in turn on the membrane through the contact region. The two models considered are a stationary (or elliptic) model and an evolutionary (or quasistatic) one. For the first model, we prove the existence of weak solutions by solving an elliptic quasivariational inequality coupled to elliptic equations. By exploring the fine properties of the variation of the contact set under nondegenerate data, we give sufficient conditions for the existence of regular solutions, and under certain contraction conditions, also a uniqueness result. We apply these results to a series of semidiscretised problems that arise as approximations of regular solutions for the evolutionary or quasistatic problem. Here, under certain conditions, we are able to prove existence for the evolutionary problem and for a special case, also the uniqueness of timedependent solutions. 
A. Alphonse, M. Hintermüller, C.N. Rautenberg, On the differentiability of the minimal and maximal solution maps of elliptic quasivariational inequalities, Journal of Mathematical Analysis and Applications, 507, DOI 10.1016/j.jmaa.2021.125732 .
Abstract
In this short note, we prove that the minimal and maximal solution maps associated to elliptic quasivariational inequalities of obstacle type are directionally differentiable with respect to the forcing term and for directions that are signed. On the way, we show that the minimal and maximal solutions can be seen as monotone limits of solutions of certain variational inequalities and that the aforementioned directional derivatives can also be characterised as the monotone limits of sequences of directional derivatives associated to variational inequalities. 
A. Alphonse, M. Hintermüller, C.N. Rautenberg, Optimal control and directional differentiability for elliptic quasivariational inequalities, SetValued and Variational Analysis. Theory and Applications. Springer, Dordrecht. English., 30 (2022), pp. 873922, DOI 10.1007/s1122802100624x .
Abstract
We focus on elliptic quasivariational inequalities (QVIs) of obstacle type and prove a number of results on the existence of solutions, directional differentiability and optimal control of such QVIs. We give three existence theorems based on an order approach, an iteration scheme and a sequential regularisation through partial differential equations. We show that the solution map taking the source term into the set of solutions of the QVI is directionally differentiable for general unsigned data, thereby extending the results of our previous work which provided a first differentiability result for QVIs in infinite dimensions. Optimal control problems with QVI constraints are also considered and we derive various forms of stationarity conditions for control problems, thus supplying among the first such results in this area. 
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. 
M. Hintermüller, K. Papafitsoros, C.N. Rautenberg, H. Sun, Dualization and automatic distributed parameter selection of total generalized variation via bilevel optimization, Numerical Functional Analysis and Optimization. An International Journal, (2022), published online on 05.05.2022, DOI 10.1080/01630563.2022.2069812 .
Abstract
Total Generalized Variation (TGV) regularization in image reconstruction relies on an infimal convolution type combination of generalized first and secondorder derivatives. This helps to avoid the staircasing effect of Total Variation (TV) regularization, while still preserving sharp contrasts in images. The associated regularization effect crucially hinges on two parameters whose proper adjustment represents a challenging task. In this work, a bilevel optimization framework with a suitable statisticsbased upper level objective is proposed in order to automatically select these parameters. The framework allows for spatially varying parameters, thus enabling better recovery in highdetail image areas. A rigorous dualization framework is established, and for the numerical solution, two Newton type methods for the solution of the lower level problem, i.e. the image reconstruction problem, and two bilevel TGV algorithms are introduced, respectively. Denoising tests confirm that automatically selected distributed regularization parameters lead in general to improved reconstructions when compared to results for scalar parameters.
Contributions to Collected Editions

A. Alphonse, M. Hintermüller, C.N. Rautenberg, Stability and sensitivity analysis for quasivariational inequalities, in: NonSmooth and ComplementarityBased 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. 183210.

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., published online on 17.02.2022, Springer, Cham, DOI 10.1007/9783030030094_661 .

D. Gahururu, M. Hintermüller, S.M. Stengl, Th.M. Surowiec, Generalized Nash equilibrium problems with partial differential operators: Theory, algorithms and risk aversion, in: NonSmooth and ComplementarityBased 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. 145181.

C. Grässle, M. Hintermüller, M. Hinze, T. Keil, Simulation and control of a nonsmooth CahnHilliard NavierStokes system with variable fluid densities, in: NonSmooth and ComplementarityBased 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. 211240.

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, pp. published online on 15.06.2022, DOI 10.1007/9783031087608_11 .

C. Geiersbach, E. LoayzaRomero, K. Welker, PDEConstrained Shape Optimization: Towards Product Shape Spaces and Stochastic Models, in: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, K. Chen, C.B. Schönlieb, X.Ch. Tai, L. Younces, eds., Springer, DOI 10.1007/9783030030094_1201 .
Abstract
Shape optimization models with one or more shapes are considered in this chapter. Of particular interest for applications are problems in which a socalled shape functional is constrained by a partial differential equation (PDE) describing the underlying physics. A connection can be made between a classical view of shape optimization and the differential geometric structure of shape spaces. To handle problems where a shape functional depends on multiple shapes, a theoretical framework is presented, whereby the optimization variable can be represented as a vector of shapes belonging to a product shape space. The multishape gradient and multishape derivative are defined, which allows for a rigorous justification of a steepest descent method with Armijo backtracking. As long as the shapes as subsets of a holdall domain do not intersect, solving a single deformation equation is enough to provide descent directions with respect to each shape. Additionally, a framework for handling uncertainties arising from inputs or parameters in the PDE is presented. To handle potentially highdimensional stochastic spaces, a stochastic gradient method is proposed. A model problem is constructed, demonstrating how uncertainty can be introduced into the problem and the objective can be transformed by use of the expectation. Finally, numerical experiments in the deterministic and stochastic case are devised, which demonstrate the effectiveness of the presented algorithms. 
J.J. Zhu, Ch. Kouridi, N. Yassine, B. Schölkopf, Adversarially robust kernel smoothing, in: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, G. CampsValls, F.J.R. Ruiz, I. Valera, eds., 151 of Proceedings of Machine Learning Research, 2022, pp. 49724994.
Abstract
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy. We adapt the integral operator using supremal convolution in convex analysis to form a novel function majorant used for enforcing robustness. Our method is simple in form and applies to general loss functions and machine learning models. Exploiting a connection with optimal transport, we prove theoretical guarantees for certified robustness under distribution shift. Furthermore, we report experiments with general machine learning models, such as deep neural networks, to demonstrate competitive performance with the stateoftheart certifiable robust learning algorithms based on the Wasserstein distance.
Preprints, Reports, Technical Reports

G. Dong, M. Hintermüller, K. Papafitsoros, A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives, Preprint no. 2964, WIAS, Berlin, 2022, DOI 10.20347/WIAS.PREPRINT.2964 .
Abstract, PDF (748 kByte)
We propose and analyze a numerical algorithm for solving a class of optimal control problems for learninginformed semilinear partial differential equations. The latter is a class of PDEs with constituents that are in principle unknown and are approximated by nonsmooth ReLU neural networks. We first show that a direct smoothing of the ReLU network with the aim to make use of classical numerical solvers can have certain disadvantages, namely potentially introducing multiple solutions for the corresponding state equation. This motivates us to devise a numerical algorithm that treats directly the nonsmooth optimal control problem, by employing a descent algorithm inspired by a bundlefree method. Several numerical examples are provided and the efficiency of the algorithm is shown. 
A. Alphonse, C. Geiersbach, M. Hintermüller, Th.M. Surowiec, Riskaverse optimal control of random elliptic VIs, Preprint no. 2962, WIAS, Berlin, 2022, DOI 10.20347/WIAS.PREPRINT.2962 .
Abstract, PDF (1541 kByte)
We consider a riskaverse optimal control problem governed by an elliptic variational inequality (VI) subject to random inputs. By deriving KKTtype optimality conditions for a penalised and smoothed problem and studying convergence of the stationary points with respect to the penalisation parameter, we obtain two forms of stationarity conditions. The lack of regularity with respect to the uncertain parameters and complexities induced by the presence of the risk measure give rise to new challenges unique to the stochastic setting. We also propose a pathfollowing stochastic approximation algorithm using variance reduction techniques and demonstrate the algorithm on a modified benchmark problem. 
G. Dong, M. Hintermüller, K. Papafitsoros, K. Völkner, Firstorder conditions for the optimal control of learninginformed nonsmooth PDEs, Preprint no. 2940, WIAS, Berlin, 2022, DOI 10.20347/WIAS.PREPRINT.2940 .
Abstract, PDF (408 kByte)
In this paper we study the optimal control of a class of semilinear elliptic partial differential equations which have nonlinear constituents that are only accessible by data and are approximated by nonsmooth ReLU neural networks. The optimal control problem is studied in detail. In particular, the existence and uniqueness of the state equation are shown, and continuity as well as directional differentiability properties of the corresponding controltostate map are established. Based on approximation capabilities of the pertinent networks, we address fundamental questions regarding approximating properties of the learninginformed controltostate map and the solution of the corresponding optimal control problem. Finally, several stationarity conditions are derived based on different notions of generalized differentiability. 
M. Hintermüller, T. Keil, Strong stationarity conditions for the optimal control of a CahnHilliardNavierStokes system, Preprint no. 2924, WIAS, Berlin, 2022, DOI 10.20347/WIAS.PREPRINT.2924 .
Abstract, PDF (290 kByte)
This paper is concerned with the distributed optimal control of a timediscrete CahnHilliardNavierStokes system with variable densities. It focuses on the doubleobstacle potential which yields an optimal control problem for a variational inequality of fourth order and the NavierStokes equation. The existence of solutions to the primal system and of optimal controls is established. The Lipschitz continuity of the constraint mapping is derived and used to characterize the directional derivative of the constraint mapping via a system of variational inequalities and partial differential equations. Finally, strong stationarity conditions are presented following an approach from Mignot and Puel.
Talks, Poster

S. Essadi, Constrained deterministic nonsmooth mean field games, GAMM 92nd Annual Meeting 2022, August 15  19, 2022, RWTH Aachen University, August 16, 2022.

S. Essadi, Constrained mean field games: analysis and algorithms, SPP1962 Annual Meeting 2022, October 24  26, 2022, Novotel Hotel Berlin Mitte, October 25, 2022.

A. Alphonse, Directional differentiability and optimal control for quasivariational inequalities (online talk), ``Partial Differential Equations and their Applications'' Seminar, University of Warwick, Mathematics Institute, UK, January 25, 2022.

A. Alphonse, Riskaverse optimal control of elliptic random variational inequalities, SPP1962 Annual Meeting 2022, October 24  26, 2022, Novotel Hotel Berlin Mitte, October 25, 2022.

M. Bongarti, Boundary feedback stabilization of a critical nonlinear JMGT equation with Neumannundissipated part of the boundary, Waves Conference 2022, July 24  29, 2022, ENSTA Institut Polytechnique de Paris, France, July 25, 2022.

M. Bongarti, Boundary feedback stabilization of a critical nonlinear JMGT equation with Neumannundissipated part of the boundary, IFIP TC7 System Modeling and Optimization, July 4  8, 2022, University of Technology, Warschau, Poland, July 4, 2022.

M. Bongarti, Nonlinear gas transport on a network of pipelines, IFIP TC7 System Modeling and Optimization, July 4  8, 2022, University of Technology, Warschau, Poland, July 4, 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.C. De Los Reyes, Bilevel learning for inverse problems, Seminar SFB 1060, April 11  14, 2022, Universität Bonn, Fachbereich Mathematik, Bonn, April 14, 2022.

J. Leake, Lorentzian polynomials on cones and the HeronRotaWelsh conjecture, Workshop "LaguerrePolya Class and Combinatorics", Mathematisches Forschungsinstitut Oberwolfach, March 18, 2022.

J. Leake, Lorentzian polynomials on cones and the HeronRotaWelsh conjecture, TU Braunschweig, June 16, 2022.

M. Theiss, Constrained MFG: analysis and algorithms, SPP1962 Annual Meeting 2022, October 24  26, 2022, Novotel Hotel Berlin Mitte, October 25, 2022.

M. Brokate, Derivatives of hysteresis operators, MURPHYS 2022, Silesian University in Opava, Czech Republic, May 30, 2022.

M. Brokate, Newton derivatives of convex functionals, Conference on Multiple Scale Systems, Silesian University in Opava, Ostravice, Czech Republic, January 16, 2022.

M. Brokate, Rate independent evolutions, Kolloquiumsvortrag, Charles University, Prag, Czech Republic, March 10, 2022.

M. Brokate, Rate independent evolutions: derivatives and control, Kolloquiumsvortrag, Universität Kiel, April 29, 2022.

M. Brokate, Strong stationarity for an optimal control problem for a rate independent evolution, Equadiff 15, Masaryk University, Brno, Czech Republic, July 12, 2022.

H. Kremer, J.J. Zhu, K. Muandet, B. Schölkopf, Functional generalized empirical likelihood estimation for conditional moment restrictions, ICML 2022: 39th International Conference on Machine Learning (Online Event), Baltimore, USA, July 18  23, 2022.

C. Geiersbach, Optimality conditions and regularization for stochastic optimization with almost sure state constraints, 15th Viennese Conference on Optimal Control and Dynamic Games, July 12  15, 2022, TU Wien, Austria, July 14, 2022.

C. Geiersbach, Optimality conditions and regularization for stochastic optimization with almost sure state constraints, International Conference on Continuous Optimization  ICCOPT/MOPTA 2022, Cluster ``PDEConstrained Optimization'', July 23  28, 2022, Lehigh University, Bethlehem, Pennsylvania, USA, July 26, 2022.

C. Geiersbach, Optimization with almost sure state constraints, GAMM 92nd Annual Meeting 2022, RWTH Aachen University, August 16, 2022.

C. Geiersbach, Gametheoretical modeling for green hydrogen markets, Future WiNS: New Energies for a Sustainable World, December 7  9, 2022, HumoldtUniversität zu Berlin, December 9, 2022.

C. Geiersbach, Optimality conditions and regularization for OUU with almost sure state constraints (online talk), SIAM Conference on Uncertainty Quantification (Hybrid Event), Minisymposium 24 ``PDEConstrained Optimization Under Uncertainty'', April 12  15, 2022, Atlanta, Georgia, USA, April 12, 2022.

C. Geiersbach, Optimality conditions and regularization for stochastic optimization with almost sure state constraints (online talk), 2022 SIAM Conference on Imaging Science (IS22) (Online Event), Minisymposium ``Stochastic Iterative Methods for Inverse Problems'', March 21  25, 2022, March 25, 2022.

C. Geiersbach, Problems and challenges in stochastic optimization (online talk), WIAS Days, March 2, 2022.

C. Geiersbach, Shape optimization under uncertainty: Challenges and algorithms, Helmut Schmidt Universität Hamburg, Mathematik im Bauingenieurwesen, April 26, 2022.

C. Geiersbach, State constraints in stochastic optimization, PGMO days 2022, EDF Lab ParisSaclay, Palaiseau, France, November 30, 2022.

M. Bongarti, Boundary stabilization of nonlinear dynamics of acoustic waves under the JMGT equation, Oberseminar Partielle Differentialgleichungen, Universität Konstanz, November 17, 2022.

M. Hintermüller, A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives (online talk), Workshop 2: Structured Optimization Models in HighDimensional Data Analysis, December 12  16, 2022, National University of Singapore, Institute for Mathematical Sciences, December 15, 2022.

M. Hintermüller, Optimization subject to learning informed PDEs, International Conference on Continuous Optimization  ICCOPT/MOPTA 2022, Cluster ``PDEConstrained Optimization'', July 23  28, 2022, Lehigh University, Bethlehem, Pennsylvania, USA, July 27, 2022.

M. Hintermüller, Optimization with learninginformed differential equation constraints (online talk), Workshop on Control Problems (Online Event), October 17, 2022  February 20, 2023, Technische Universität Dortmund, October 17, 2022.

M. Hintermüller, Optimization with learninginformed differential equations, Robustness and Resilience in Stochastic Optimization and Statistical Learning: Mathematical Foundations, May 20  24, 2022, Ettore Majorana Foundation and Centre for Scientific Culture, Erice, Italy, May 24, 2022.

M. Hintermüller, PDEconstrained optimization with learninginformed structures (online talk), Optimization in Oslo (OiO) Seminar, Simula Research Laboratory, Norway, December 7, 2022.

K. Papafitsoros, Automatic distributed parameter selection of regularization functionals via bilevel optimization (online talk), SIAM Conference on Imaging Science (Online Workshop), Minisymposium ``Statistics and Structure for Parameter and Image Restoration'', March 21  25, 2022, March 22, 2022.

K. Papafitsoros, Total variation methods in image reconstruction, Institute Colloquium, Foundation for Research and Technology Hellas (IACMFORTH), Institute of Applied and Computational Mathematics, Heraklion, Greece, May 3, 2022.

K. Papafitsoros, Optimization with learninginformed nonsmooth differential equation constraints, Second Congress of Greek Mathematicians SCGM2022, Session Numerical Analysis & Scientific Computing, July 4  8, 2022, National Technical University of Athens, July 6, 2022.

C. Sirotenko, Dictionary learning for an in inverse problem in quantitative mri, GAMM 92nd Annual Meeting 2022, RWTH Aachen University, August 16, 2022.

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

M. Brokate, C. Christof, Strong stationarity conditions for optimal control problems governed by a ratendependent Evolution Variational Inequality, Preprint no. arXiv:2205.01196, Cornell University Library, arXiv.org, 2022, DOI 10.48550/arXiv.2205.01196 .
Research Groups
 Partial Differential Equations
 Laser Dynamics
 Numerical Mathematics and Scientific Computing
 Nonlinear Optimization and Inverse Problems
 Interacting Random Systems
 Stochastic Algorithms and Nonparametric Statistics
 Thermodynamic Modeling and Analysis of Phase Transitions
 Nonsmooth Variational Problems and Operator Equations