| 2019Sergio Conti, Martin Lenz, Nora Lüthen, Martin Rumpf and Barbara Zwicknagl
Geometry of martensite needles in shape memory alloys
2019
https://arxiv.org/abs/1912.02274
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| Margherita Disertori, Franz Merkl and Silke W. W. Rolles
Martingales and some generalizations arising from the supersymmetric hyperbolic sigma model
ALEA Lat. Am. J. Probab. Math. Stat., 16(1): 179--209 2019
https://ui.adsabs.harvard.edu/abs/2017arXiv171002308D
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| Margherita Disertori, Franz Merkl and Silke W. W. Rolles
Martingales and some generalizations arising from the supersymmetric hyperbolic sigma model
ALEA Lat. Am. J. Probab. Math. Stat., 16 (Vol 1): 179--209 2019
https://doi.org/10.30757/ALEA.v16-07
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| Patrick W. Dondl, Patrina S. P. Poh, Martin Rumpf and Stefan Simon
Simultaneous elastic shape optimization for a domain splitting in bone tissue engineering
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2227): 20180718 2019
10.1098/rspa.2018.0718
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| Alexander Effland, Sebastian Neumayer and Martin Rumpf
Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds
2019
https://arxiv.org/abs/1902.10930
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| 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
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| 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
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| 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
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| Muhittin Mungan, Srikanth Sastry, Karin Dahmen and Ido Regev
Networks and Hierarchies: How Amorphous Materials Learn to Remember
Phys. Rev. Lett., 123: 178002 2019
https://link.aps.org/doi/10.1103/PhysRevLett.123.178002
Abstract: We consider the slow and athermal deformations of amorphous solids and show how the ensuing sequence of discrete plastic rearrangements can be mapped onto a directed network. The network topology reveals a set of highly connected regions joined by occasional one-way transitions. The highly connected regions include hierarchically organized hysteresis cycles and subcycles. At small to moderate strains this organization leads to near-perfect return point memory. The transitions in the network can be traced back to localized particle rearrangements (soft spots) that interact via Eshelby-type deformation fields. By linking topology to dynamics, the network representations provide new insight into the mechanisms that lead to reversible and irreversible behavior in amorphous solids. |
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| C. Rieger and H. Wendland
Sampling Inequalities for Anisotropic Tensor Product Grids
IMA Journal of Numerical Analysis 2019
https://doi.org/10.1093/imanum/dry080
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| Filip Rindler, Sebastian Schwarzacher and Juan J. L. Velázquez
Two-speed solutions to non-convex rate-independent systems
arXiv e-prints: arXiv:1907.05035 2019
https://ui.adsabs.harvard.edu/abs/2019arXiv190705035R
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| 2018Sergio Conti, Martin Rumpf, Rüdiger Schultz and Sascha Tölkes
Stochastic Dominance Constraints in Elastic Shape Optimization
SIAM J. Control Optim., 56: 3021-3034 2018
10.1137/16M108313X
Abstract: This paper deals with shape optimization for elastic materials under stochastic loads. It transfers the paradigm of stochastic dominance, which allows for flexible risk aversion via comparison with benchmark random variables, from finite-dimensional stochastic programming to shape optimization. Rather than handling risk aversion in the objective, this enables risk aversion by including dominance constraints that single out subsets of nonanticipative shapes which compare favorably to a chosen stochastic benchmark. This new class of stochastic shape optimization problems arises by optimizing over such feasible sets. The analytical description is built on risk-averse cost measures. The underlying cost functional is of compliance type plus a perimeter term, in the implementation shapes are represented by a phase field which permits an easy estimate of a regularized perimeter. The analytical description and the numerical implementation of dominance constraints are built on risk-averse measures for the cost functional. A suitable numerical discretization is obtained using finite elements both for the displacement and the phase field function. Different numerical experiments demonstrate the potential of the proposed stochastic shape optimization model and in particular the impact of high variability of forces or probabilities in the different realizations. |
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| Sergio Conti, Martin Lenz, Matthäus Pawelczyk and Martin Rumpf
Homogenization in magnetic-shape-memory polymer composites
In Volker Schulz and Diaraf Seck, editor, Shape Optimization, Homogenization and Optimal Control, Volume 169 of International Series of Numerical Mathematics
page 1-17.
Publisher: Birkhäuser, Cham
2018
10.1007/978-3-319-90469-6_1
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| Sergio Conti, Benedict Geihe, Martin Lenz and Martin Rumpf
A posteriori modeling error estimates in the optimization of two-scale elastic composite materials
ESAIM: Mathematical Modelling and Numerical Analysis, 52: 1457-1476 2018
10.1051/m2an/2017004
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| D. Dũng, M. Griebel, V. N. Huy and C. Rieger
$\varepsilon$-dimension in infinite dimensional hyperbolic cross approximation and application to parametric elliptic PDEs
Journal of Complexity, 46: 66--89 2018
10.1016/j.jco.2017.12.001
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| 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
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| M. Griebel, C. Rieger and B. Zwicknagl
Regularized Kernel-Based Reconstruction in Generalized Besov Spaces
Foundations of Computational Mathematics, 18(2): 459--508 2018
10.1007/s10208-017-9346-z
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| Michael Griebel, Christian Rieger and Peter Zaspel
Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations
2018
https://ins.uni-bonn.de/media/public/publication-media/INSPreprint1813.pdf
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| Celia Reina, Landry Fokoua Djodom, Michael Ortiz and Sergio Conti
Kinematics of elasto-plasticity: Validity and limits of applicability of $F=F_eF_p$ for general three-dimensional deformations
Journal of the Mechanics and Physics of Solids, 121: 99--113 2018
10.1016/j.jmps.2018.07.006
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| 2017Benjamin 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
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