Prof. Dr. Martin Burger

2024

• Fazeny, A., Tenbrinck, D., Lukin, K., & Burger, M. (2024). Hypergraph p-Laplacians and Scale Spaces. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-024-01183-0

2023

• Burger, M., & Rossi, A. (2023). ANALYSIS OF KINETIC MODELS FOR LABEL SWITCHING AND STOCHASTIC GRADIENT DESCENT. Kinetic and Related Models. https://doi.org/10.3934/krm.2023005
• Fazeny, A., Tenbrinck, D., & Burger, M. (2023). Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks. In SSVM 2023: Scale Space and Variational Methods in Computer Vision (pp. 677-690). Santa Margherita di Pula, IT.
• Kabri, S., Roith, T., Tenbrinck, D., & Burger, M. (2023). Resolution-Invariant Image Classification Based on Fourier Neural Operators. In Scale Space and Variational Methods in Computer Vision (pp. 236-249). Santa Margherita di Pula, IT.

2022

• Bhattacharjee, P., Burger, M., Boerner, A., & Morgenshtern, V. (2022). Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2022.3163552
• Bozorgnia, F., Burger, M., & Fotouhi, M. (2022). ON A CLASS OF SINGULARLY PERTURBED ELLIPTIC SYSTEMS WITH ASYMPTOTIC PHASE SEGREGATION. Discrete and Continuous Dynamical Systems. https://doi.org/10.3934/dcds.2022023
• Bruna, M., Burger, M., Esposito, A., & Schulz, S. (2022). WELL-POSEDNESS OF AN INTEGRO-DIFFERENTIAL MODEL FOR ACTIVE BROWNIAN PARTICLES. SIAM Journal on Mathematical Analysis, 54(6), 5662-5697. https://doi.org/10.1137/21M1462039
• Bruna, M., Burger, M., Esposito, A., & Schulz, S.M. (2022). PHASE SEPARATION IN SYSTEMS OF INTERACTING ACTIVE BROWNIAN PARTICLES. SIAM Journal on Applied Mathematics, 82(4), 1635-1660. https://doi.org/10.1137/21M1452524
• Bruna, M., Burger, M., Pietschmann, J.F., & Wolfram, M.T. (2022). Active Crowds. In (pp. 35-73). Birkhauser.
• Bungert, L., & Burger, M. (2022). Gradient flows and nonlinear power methods for the computation of nonlinear eigenfunctions. In Emmanuel Trélat, Enrique Zuazua, Enrique Zuazua, Enrique Zuazua (Eds.), (pp. 427-465). Elsevier B.V..
• Bungert, L., Roith, T., Tenbrinck, D., & Burger, M. (2022). A Bregman Learning Framework for Sparse Neural Networks. Journal of Machine Learning Research.
• Burger, M. (2022). KINETIC EQUATIONS FOR PROCESSES ON CO-EVOLVING NETWORKS. Kinetic and Related Models. https://doi.org/10.3934/krm.2021051
• Hopf, K., & Burger, M. (2022). On multi-species diffusion with size exclusion. Nonlinear Analysis - Theory Methods & Applications, 224. https://doi.org/10.1016/j.na.2022.113092
• Werner, P., Burger, M., Frank, F., & Garcke, H. (2022). A diffuse interface model for cell blebbing including membrane-cortex coupling with linker dynamics. SIAM Journal on Applied Mathematics, 3(82), 1091-1112. https://doi.org/10.1137/21m1433642

2021

• Burger, M. (2021). Network Structured Kinetic Models of Social Interactions. Vietnam Journal of Mathematics. https://doi.org/10.1007/s10013-021-00505-8
• Burger, M., Hauptmann, A., Helin, T., Hyvonen, N., & Puska, J.P. (2021). Sequentially optimized projections in x-ray imaging *. Inverse Problems, 37(7). https://doi.org/10.1088/1361-6420/ac01a4
• Burger, M., Kreusser, L.M., & Totzeck, C. (2021). Mean-field optimal control for biological pattern formation. Esaim-Control Optimisation and Calculus of Variations, 27. https://doi.org/10.1051/cocv/2021034
• Burger, M., Pinnau, R., Totzeck, C., & Tse, O. (2021). Mean-field optimal control and optimality conditions in the space of probability measures. SIAM Journal on Control and Optimization, 59(2), 977-1006. https://doi.org/10.1137/19M1249461
• Burger, M., Weinan, E., Ruthotto, L., & Osher, S.J. (2021). Connections between deep learning and partial differential equations. European Journal of Applied Mathematics, 32(3), 395-396. https://doi.org/10.1017/S0956792521000085
• Koulouri, A., Heins, P., & Burger, M. (2021). Adaptive Superresolution in Deconvolution of Sparse Peaks. IEEE Transactions on Signal Processing, 69, 165-178. https://doi.org/10.1109/TSP.2020.3037373
• Schwinn, L., Nguyen, A., Raab, R., Bungert, L., Tenbrinck, D., Zanca, D.,... Eskofier, B. (2021). Identifying untrustworthy predictions in neural networks by geometric gradient analysis. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). Online.
• Schwinn, L., Nguyen, A., Raab, R., Zanca, D., Eskofier, B., Tenbrinck, D., & Burger, M. (2021). Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN). Online.

2020

• Arridge, S.R., Burger, M., & Ehrhardt, M.J. (2020). Preface to special issue on joint reconstruction and multi-modality/multi-spectral imaging. Inverse Problems, 36(2). https://doi.org/10.1088/1361-6420/ab4abb
• Bruna, M., Burger, M., & Carrillo, J.A. (2020). Coarse graining of a Fokker-Planck equation with excluded volume effects preserving the gradient flow structure. European Journal of Applied Mathematics. https://doi.org/10.1017/S0956792520000285
• Bungert, L., Burger, M., Korolev, Y., & Schönlieb, C.B. (2020). Variational regularisation for inverse problems with imperfect forward operators and general noise models. Inverse Problems, 36(12). https://doi.org/10.1088/1361-6420/abc531
• Bungert, L., Korolev, Y., & Burger, M. (2020). Structural analysis of an L-infinity variational problem and relations to distance functions. Pure and Applied Analysis, 2(3), 703 - 738. https://doi.org/10.2140/paa.2020.2.703
• Burger, M., Carrillo, J.A., Pietschmann, J.-F., & Schmidtchen, M. (2020). Segregation effects and gap formation in cross-diffusion models. Interfaces and Free Boundaries, 22(2), 175-203. https://doi.org/10.4171/IFB/438
• Burger, M., Friele, P., & Pietschmann, J.F. (2020). On a reaction-cross-diffusion system modeling the growth of glioblastoma. SIAM Journal on Applied Mathematics, 80(1), 160-182. https://doi.org/10.1137/18M1194559
• Burger, M., Humpert, I., & Pietschmann, J.-F. (2020). ON FOKKER-PLANCK EQUATIONS WITH IN- AND OUTFLOW OF MASS. Kinetic and Related Models, 13(2), 249-277. https://doi.org/10.3934/krm.2020009
• Burger, M., Laurencot, P., & Trescases, A. (2020). Delayed blow-up for chemotaxis models with local sensing. Journal of the London Mathematical Society-Second Series. https://doi.org/10.1112/jlms.12420
• Burger, M., Pietschmann, J.-F., & Wolfram, M.-T. (2020). Data assimilation in price formation. Inverse Problems, 36(6). https://doi.org/10.1088/1361-6420/ab6d5a
• Burger, M., Pinnau, R., Totzeck, C., Tse, O., & Roth, A. (2020). Instantaneous control of interacting particle systems in the mean-field limit. Journal of Computational Physics, 405. https://doi.org/10.1016/j.jcp.2019.109181
• Burger, M., Resmerita, E., & Benning, M. (2020). An entropic Landweber method for linear ill-posed problems. Inverse Problems, 36(1). https://doi.org/10.1088/1361-6420/ab5c49
• Drechsler, M., Lang, L.F., Al-Khatib, L., Dirks, H., Burger, M., Schönlieb, C.B., & Palacios, I.M. (2020). Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in the Drosophila oocyte. Molecular Biology of the Cell, 31(12), 1246-1258. https://doi.org/10.1091/mbc.E19-08-0440
• Gross-Thebing, S., Truszkowski, L., Tenbrinck, D., Sanchez-Iranzo, H., Camelo, C., Westerich, K.J.,... Raz, E. (2020). Using migrating cells as probes to illuminate features in live embryonic tissues. Science Advances, 6(49). https://dx.doi.org/10.1126/sciadv.abc5546
• Hong, B.W., Koo, J., Burger, M., & Soatto, S. (2020). Adaptive Regularization of Some Inverse Problems in Image Analysis. IEEE Transactions on Image Processing, 29, 2507-2521. https://doi.org/10.1109/TIP.2019.2960587