Prof. Dr. Martin Burger

Thomson Researcher ID: D-9928-2012



Organisation


Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)


Awards / Honours


2014 : ERC Consolidator Grant
2009 : Calderón-Preis
1999 : Preis für Besondere Studentische Leistungen 1999



Project lead


PPP Frankreich 2019 Phase I
Prof. Dr. Martin Burger
(01/01/2019 - 31/12/2020)

(Nonlocal Methods for Arbitrary Data Sources):
NoMADS: Nonlocal Methods for Arbitrary Data Sources
Prof. Dr. Martin Burger
(01/10/2018 - 28/02/2022)

MED4D: "Verbundprojekt MED4D: Dynamische Medizinische Bildgebung: Modellierung und Analyse medizinischer Daten für verbesserte Diagnose, Überwachung und Arzneimittelentwicklung"
Prof. Dr. Martin Burger
(01/12/2016 - 30/11/2019)

DAAD Exchange Service: PPP Finnland 2017: Bayesian Inverse Problems in Banach Space
Prof. Dr. Martin Burger
(25/01/2015 - 31/12/2017)

LifeInverse: Variational Methods for Dynamic Inverse Problems in the Life Sciences
Prof. Dr. Martin Burger
(01/03/2014 - 28/02/2019)


Project member


IntComSin: Interfaces, complex structures, and singular limits in continuum mechanics
Prof. Dr. Günther Grün
(01/04/2018 - 30/09/2022)


Other Research Activities


Editorship of a scientific journal / series
Prof. Dr. Martin Burger
Editorial head - European Journal of Applied Mathematics (ISSN: 1469-4425)
(01/01/2017)


Publications (Download BibTeX)

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Werner, P., Burger, M., & Pietschmann, J.-F. (2019). A PDE model for bleb formation and interaction with linker proteins. (Unpublished, Submitted).
Bungert, L., & Burger, M. (2019). Asymptotic Profiles of Nonlinear Homogeneous Evolution Equations of Gradient Flow Type. (Unpublished, Submitted).
Burger, M., Korolev, Y., Schönlieb, C.B., & Stollenwerk, C. (2019). A Total Variation Based Regularizer Promoting Piecewise-Lipschitz Reconstructions. In Jan Lellmann, Jan Modersitzki, Martin Burger (Eds.), Lecture Notes in Computer Science (pp. 485-497). Hofgeismar, DE: Springer Verlag.
Bungert, L., Burger, M., & Tenbrinck, D. (2019). Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. In Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. (pp. 291-302). Springer Verlag.
Burger, M., Korolev, Y., & Rasch, J. (2019). Convergence rates and structure of solutions of inverse problems with imperfect forward models. Inverse Problems, 35(2). https://dx.doi.org/10.1088/1361-6420/aaf6f5
Bungert, L., & Burger, M. (2019). Solution paths of variational regularization methods for inverse problems. Inverse Problems. https://dx.doi.org/10.1088/1361-6420/ab1d71
Burger, M. (2018). A Variational Model for Joint Motion Estimation and Image Reconstruction. Siam Journal on Imaging Sciences, 11(1), 94-128. https://dx.doi.org/10.1137/16M1084183
Burger, M. (2018). Dynamic inverse problems: modelling-regularization-numerics Preface. Inverse Problems, 34(4). https://dx.doi.org/10.1088/1361-6420/aab0f5
Burger, M. (2018). Dynamic MRI reconstruction from undersampled data with an anatomical prescan. Inverse Problems, 34(7). https://dx.doi.org/10.1088/1361-6420/aac3af
Burger, M. (2018). Dynamic SPECT reconstruction with temporal edge correlation. Inverse Problems, 34(1). https://dx.doi.org/10.1088/1361-6420/aa9a94

Last updated on 2019-22-01 at 17:51