Preprints - IAM Publications


	2024 J.W. Jang E.Dematte and JJL Velázquez Compactness and existence theory for a general class of stationary radiative transfer equations 2024 https://arxiv.org/abs/2401.12828

	2023 Manuel Esser and Anna Kraut Effective growth rates in a periodically changing environment: From mutation to invasion 2023 https://arxiv.org/abs/2310.20509

	2021 Manuel Esser and Anna Kraut A general multi-scale description of metastable adaptive motion across fitness valleys 2021 https://arxiv.org/abs/2112.12675

	2019 Alexander Effland, Sebastian Neumayer and Martin Rumpf Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds 2019 https://arxiv.org/abs/1902.10930

	Alexander Effland, Erich Kobler, Thomas Pock, Marko Rajković and Martin Rumpf Image Morphing in Deep Feature Spaces: Theory and Applications 2019 https://arxiv.org/abs/1910.12672

	Alexander Effland, Erich Kobler, Thomas Pock and Martin Rumpf Time Discrete Geodesics in Deep Feature Spaces for Image Morphing In Lellmann, Jan and Burger, Martin and Modersitzki, Jan, editor, Scale Space and Variational Methods in Computer Vision page 171--182. Publisher: Springer International Publishing 2019 https://dx.doi.org/10.1007/978-3-030-22368-7_14

	Alexander Effland, Erich Kobler, Anne Brandenburg, Teresa Klatzer, Leonie Neuhäuser, Michael Hölzel, Jennifer Landsberg, Thomas Pock and Martin Rumpf Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data International Journal of Computer Assisted Radiology and Surgery, 14(4): 587--599 2019 https://dx.doi.org/10.1007/s11548-019-01919-z

	Matthias Erbar, Jan Maas and Melchior Wirth On the geometry of geodesics in discrete optimal transport Calc. Var. Partial Differential Equations, 58(1): Art. 19, 19 2019 10.1007/s00526-018-1456-1

	Matthias Erbar and Karl-Theodor Sturm Riggidity of cones with bounded curvature to appear in JEMS, arxiv e-print 1712.08093 2019 https://arxiv.org/abs/1712.08093

	2018 Alexander Effland, Martin Rumpf and Florian Schäfer Image extrapolation for the time discrete metamorphosis model -- existence and applications SIAM J. Imaging Sci. 2018 https://arxiv.org/abs/1705.04490

	C. Eichenberg Special Solutions to a Nonlinear Coarsening Model with Local Interactions Journal of NonLinear Science 2018 10.1007/s00332-018-9519-1

	Matthias Erbar and Nicolas Juillet Smoothing and non-smoothing via a flow tangent to the Ricci flow J. Math. Pures Appl. (9), 110: 123--154 2018 10.1016/j.matpur.2017.07.006

	Matthias Erbar and Max Fathi Poincaré, modified logarithmic Sobolev and isoperimetric inequalities for Markov chains with non-negative Ricci curvature J. Funct. Anal., 274(11): 3056--3089 2018 10.1016/j.jfa.2018.03.011

	Matthias Erbar and Eva Kopfer Super Ricci flows for Markov chains arxiv e-print 1805.06703 2018 https://arxiv.org/abs/1805.06703

	Matthias Erbar, Martin Huesmann and Thomas Leblé The one-dimensional log-gas free energy has a unique minimiser arxiv e-print 1812.06929 2018 https://arxiv.org/abs/1812.06929

	2017 Benjamin Berkels, Michael Buchner, Alexander Effland, Martin Rumpf and Steffen Schmitz-Valckenberg GPU Based Image Geodesics for Optical Coherence Tomography In Bildverarbeitung für die Medizin, Informatik aktuell page 68--73. Publisher: Springer 2017 http://dx.doi.org/10.1007/978-3-662-54345-0_21

	Alexander Effland, Martin Rumpf and Florian Schäfer Time discrete extrapolation in a Riemannian space of images In Proc. of International Conference on Scale Space and Variational Methods in Computer Vision, Volume 10302 page 473--485. Publisher: Springer, Cham 2017 https://dx.doi.org/10.1007/978-3-319-58771-4_38

	Matthias Erbar, Martin Rumpf, Bernhard Schmitzer and Stefan Simon Computation of Optimal Transport on Discrete Metric Measure Spaces Unknown https://arxiv.org/abs/1707.06859

	Matthias Erbar A gradient flow approach to the Boltzmann equation arxiv e-print 1603.0540 2017 https://arxiv.org/abs/1603.0540

	2015 Benjamin Berkels, Alexander Effland and Martin Rumpf A Posteriori Error Control for the Binary Mumford-Shah Model ArXiv Preprint 2015 http://arxiv.org/abs/1505.05284 Abstract: The binary Mumford-Shah model is a widespread tool for image segmentation and can be considered as a basic model in shape optimization with a broad range of applications in computer vision, ranging from basic segmentation and labeling to object reconstruction. This paper presents robust a posteriori error estimates for a natural error quantity, namely the area of the non properly segmented region. To this end, a suitable strictly convex and non-constrained relaxation of the originally non-convex functional is investigated and Repin's functional approach for a posteriori error estimation is used to control the numerical error for the relaxed problem in the $L^2$-norm. In combination with a suitable cut out argument, a fully practical estimate for the area mismatch is derived. This estimate is incorporated in an adaptive meshing strategy. Two different adaptive primal-dual finite element schemes, and the most frequently used finite difference discretization are investigated and compared. Numerical experiments show qualitative and quantitative properties of the estimates and demonstrate their usefulness in practical applications.

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