Martin Eigel - RankMe

54 papers C 1Journal 49Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A tensor network formalism for neuro-symbolic AI. CoRR Alex Goessmann, Janina Schütte, Maximilian Fröhlich, Martin Eigel
2026	J	`jnl`	Learning Transient Convective Heat Transfer with Geometry Aware World Models. CoRR Onur Tanil Doganay, Alexander Klawonn, Martin Eigel, Hanno Gottschalk
2026	J	`jnl`	Multilevel Sparse Tensor Approximation for High-Dimensional Parametric PDEs. CoRR Martin Eigel, Philipp Trunschke, Dana Wrischnig
2026	J	`jnl`	Reverse Diffusion Sampling with Tensor Train Approximations of Hamilton-Jacobi-Bellman Equations. SIAM J. Sci. Comput. Robert Gruhlke, David Sommer, Max Kirstein, Martin Eigel, Claudia Schillings
2025	J	`jnl`	A Convergent Adaptive Finite Element Stochastic Galerkin Method Based on Multilevel Expansions of Random Fields. SIAM J. Numer. Anal. Markus Bachmayr, Martin Eigel, Henrik Eisenmann, Igor Voulis
2025	J	`jnl`	An Eulerian Approach to Regularized JKO Scheme with Low-Rank Tensor Decompositions for Bayesian Inversion. J. Sci. Comput. Vitalii Aksenov, Martin Eigel
2025	J	`jnl`	Approximation and learning with compositional tensor trains. CoRR Martin Eigel, Charles Miranda, Anthony Nouy, David Sommer
2025	J	`jnl`	Multi-level Neural Networks for high-dimensional parametric obstacle problems. CoRR Martin Eigel, Cosmas Heiß, Janina E. Schütte
2025	—	`conf`	Sampling from Boltzmann Densities with Physics Informed Low-Rank Formats. SSVM (1) Paul Hagemann, Janina Schütte, David Sommer, Martin Eigel, Gabriele Steidl
2024	J	`jnl`	A convergent adaptive finite element stochastic Galerkin method based on multilevel expansions of random fields. CoRR Markus Bachmayr, Martin Eigel, Henrik Eisenmann, Igor Voulis
2024	J	`jnl`	Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation. CoRR Janina E. Schütte, Martin Eigel
2024	J	`jnl`	An Eulerian approach to regularized JKO scheme with low-rank tensor decompositions for Bayesian inversion. CoRR Vitalii Aksenov, Martin Eigel
2024	J	`jnl`	Functional SDE approximation inspired by a deep operator network architecture. CoRR Martin Eigel, Charles Miranda
2024	J	`jnl`	Generative Modelling with Tensor Train approximations of Hamilton-Jacobi-Bellman equations. CoRR David Sommer, Robert Gruhlke, Max Kirstein, Martin Eigel, Claudia Schillings
2024	J	`jnl`	Less Interaction with Forward Models in Langevin Dynamics: Enrichment and Homotopy. SIAM J. Appl. Dyn. Syst. Martin Eigel, Robert Gruhlke, David Sommer
2024	J	`jnl`	Multilevel CNNs for Parametric PDEs based on Adaptive Finite Elements. CoRR Janina E. Schütte, Martin Eigel
2024	J	`jnl`	Sampling from Boltzmann densities with physics informed low-rank formats. CoRR Paul Hagemann, Janina Schütte, David Sommer, Martin Eigel, Gabriele Steidl
2023	J	`jnl`	Adaptive Nonintrusive Reconstruction of Solutions to High-Dimensional Parametric PDEs. SIAM J. Sci. Comput. Martin Eigel, Nando Farchmin, Sebastian Heidenreich, Philipp Trunschke
2023	J	`jnl`	Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures. CoRR Martin Eigel, Charles Miranda, Janina Schütte, David Sommer
2023	J	`jnl`	Convergence of adaptive Galerkin FEM for parametric PDEs with lognormal coefficients. CoRR Martin Eigel, Nando Farchmin
2023	J	`jnl`	Dynamical low-rank approximations of solutions to the Hamilton-Jacobi-Bellman equation. Numer. Linear Algebra Appl. Martin Eigel, Reinhold Schneider, David Sommer
2023	J	`jnl`	Multilevel CNNs for Parametric PDEs. CoRR Cosmas Heiß, Ingo Gühring, Martin Eigel
2023	J	`jnl`	Multilevel CNNs for Parametric PDEs. J. Mach. Learn. Res. Cosmas Heiß, Ingo Gühring, Martin Eigel
2023	J	`jnl`	Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats. SIAM J. Financial Math. Christian Bayer, Martin Eigel, Leon Sallandt, Philipp Trunschke
2023	J	`jnl`	Weighted sparsity and sparse tensor networks for least squares approximation. CoRR Philipp Trunschke, Anthony Nouy, Martin Eigel
2022	J	`jnl`	Less interaction with forward models in Langevin dynamics. CoRR Martin Eigel, Robert Gruhlke, David Sommer
2022	J	`jnl`	Low-rank Wasserstein polynomial chaos expansions in the framework of optimal transport. CoRR Robert Gruhlke, Martin Eigel
2022	J	`jnl`	Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion. Stat. Comput. Martin Eigel, Robert Gruhlke, Manuel Marschall
2022	J	`jnl`	On the Convergence of Adaptive Stochastic Collocation for Elliptic Partial Differential Equations with Affine Diffusion. SIAM J. Numer. Anal. Martin Eigel, Oliver G. Ernst, Björn Sprungk, Lorenzo Tamellini
2022	C	`conf`	OptTopo: Automated set-point optimization for coupled systems using topology information. CoDIT Gregor Thiele, Theresa Johanni, David Sommer, Martin Eigel, Jörg Krüger
2022	—	`conf`	Tensor-Train Kernel Learning for Gaussian Processes. COPA Max Kirstein, David Sommer, Martin Eigel
2022	J	`jnl`	Topology Optimisation under Uncertainties with Neural Networks. Algorithms Martin Eigel, Marvin Haase, Johannes Neumann
2021	J	`jnl`	Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs. CoRR Martin Eigel, Nando Farchmin, Sebastian Heidenreich, Philipp Trunschke
2021	J	`jnl`	Efficient approximation of high-dimensional exponentials by tensornetworks. CoRR Martin Eigel, Nando Farchmin, Sebastian Heidenreich, Philipp Trunschke
2021	J	`jnl`	Pricing high-dimensional Bermudan options with hierarchical tensor formats. CoRR Christian Bayer, Martin Eigel, Leon Sallandt, Philipp Trunschke
2020	J	`jnl`	Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations. Numerische Mathematik Martin Eigel, Manuel Marschall, Max Pfeffer, Reinhold Schneider
2020	J	`jnl`	An Adaptive Stochastic Galerkin Tensor Train Discretization for Randomly Perturbed Domains. SIAM/ASA J. Uncertain. Quantification Martin Eigel, Manuel Marschall, Michael D. Multerer
2020	J	`jnl`	Comparison of various uncertainty models with experimental investigations regarding the failure of plates with holes. Reliab. Eng. Syst. Saf. Martin Drieschner, Yuri Petryna, Robert Gruhlke, Martin Eigel, Dietmar Hömberg
2020	J	`jnl`	Convergence bounds for empirical nonlinear least-squares. CoRR Martin Eigel, Reinhold Schneider, Philipp Trunschke
2020	J	`jnl`	Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion. CoRR Martin Eigel, Robert Gruhlke, Manuel Marschall
2020	J	`jnl`	On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion. CoRR Martin Eigel, Oliver G. Ernst, Björn Sprungk, Lorenzo Tamellini
2019	J	`jnl`	Non-intrusive Tensor Reconstruction for High-Dimensional Random PDEs. Comput. Methods Appl. Math. Martin Eigel, Johannes Neumann, Reinhold Schneider, Sebastian Wolf
2019	J	`jnl`	Variational Monte Carlo - bridging concepts of machine learning and high-dimensional partial differential equations. Adv. Comput. Math. Martin Eigel, Reinhold Schneider, Philipp Trunschke, Sebastian Wolf
2018	J	`jnl`	Reproducing kernel Hilbert spaces and variable metric algorithms in PDE-constrained shape optimization. Optim. Methods Softw. Martin Eigel, Kevin Sturm
2017	J	`jnl`	Adaptive stochastic Galerkin FEM with hierarchical tensor representations. Numerische Mathematik Martin Eigel, Max Pfeffer, Reinhold Schneider
2017	J	`jnl`	SDE Based Regression for Linear Random PDEs. SIAM J. Sci. Comput. Felix Anker, Christian Bayer, Martin Eigel, Marcel Ladkau, Johannes Neumann, John Schoenmakers
2016	J	`jnl`	An Adaptive Multilevel Monte Carlo Method with Stochastic Bounds for Quantities of Interest with Uncertain Data. SIAM/ASA J. Uncertain. Quantification Martin Eigel, Christian Merdon, Johannes Neumann
2016	J	`jnl`	Equilibration a Posteriori Error Estimation for Convection-Diffusion-Reaction Problems. J. Sci. Comput. Martin Eigel, Christian Merdon
2016	J	`jnl`	Local Equilibration Error Estimators for Guaranteed Error Control in Adaptive Stochastic Higher-Order Galerkin Finite Element Methods. SIAM/ASA J. Uncertain. Quantification Martin Eigel, Christian Merdon
2016	J	`jnl`	Reliable Averaging for the Primal Variable in the Courant FEM and Hierarchical Error Estimators on Red-Refined Meshes. Comput. Methods Appl. Math. Carsten Carstensen, Martin Eigel
2015	J	`jnl`	Simulation of Composite Materials by a Network FEM with Error Control. Comput. Methods Appl. Math. Martin Eigel, Daniel Peterseim
2014	J	`jnl`	Functional A Posteriori Error Estimation for Stationary Reaction-Convection-Diffusion Problems. Comput. Methods Appl. Math. Martin Eigel, Tatiana Samrowski
2008	—	`conf`	A Definition of Cellular Interface Problems. Workshop on Membrane Computing Markus Kirkilionis, Mirela Domijan, Martin Eigel, Erwin George, Mike Li, Luca Sbano
2004	—	`conf`	Spatial Modeling and Simulation of Diffusion in Nuclei of Living Cells. CMSB Dietmar Volz, Martin Eigel, Chaitanya A. Athale, Peter Bastian, Harald Hermann, Constantin Kappel, Roland Eils