Prof. Dr. Martin Burger Department of Mathematics martin.burger@fau.dehttps://www.math.fau.de/modellierung-und-numerik/ Research projects Image Reconstruction, Image Analysis, Mathematical Modelling, Advanced Methods for Large Scale Data Analysis Deep learning based reconstruction methods for tomography Motion-corrected image reconstruction methods in PET, SPECT, MR – High-throughput analysis of cell migration in zebrafish embryos Current projects Current projects Maschinelles Lernen bei korrelativer MR und Hochdurchsatz-NanoCT (Third Party Funds Single) Term: 1. April 2020 - 31. March 2023Funding source: Bundesministerium für Bildung und Forschung (BMBF) →More information Recent publications Recent publications 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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.doi.org/10.1137/21m1433642 2021 Burger, M. (2021). Network Structured Kinetic Models of Social Interactions. Vietnam Journal of Mathematics. https://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.doi.org/10.1109/TIP.2019.2960587 Related Research Fields Imaging Contact:Email: martin.burger@fau.deWeb site: https://www.math.fau.de/modellierung-und-numerik/
Research projects
Current projects
Maschinelles Lernen bei korrelativer MR und Hochdurchsatz-NanoCT
(Third Party Funds Single)
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
Recent publications
2022
2021
2020
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