5 papers
Effective permeabilities for flow through anisotropic microscopic geometries
Loïc Balazi, Fabian Holzberger, Stephan B. Lunowa +3
This work develops a computational and theoretical framework for determining effective permeabilities in anisotropic microscopic geometries containing dense, fibre-like obstacles,…
Neural Network Acceleration of Iterative Methods for Nonlinear Schrödinger Eigenvalue Problems
Daniel Peterseim, Jan-F. Pietschmann, Jonas Püschel +1
We present a novel approach to accelerate iterative methods to solve nonlinear Schrödinger eigenvalue problems using neural networks. Nonlinear eigenvector problems are fundamental…
Neural Network Enhanced Polyconvexification of Isotropic Energy Densities in Computational Mechanics
Loïc Balazi, Timo Neumeier, Malte A. Peter +1
We present a neural network approach for fast evaluation of parameter-dependent polyconvex envelopes, which are crucial in computational mechanics. Our method uses a neural network…
Energy-Adaptive Riemannian Conjugate Gradient Method for Density Functional Theory
Daniel Peterseim, Jonas Püschel, Tatjana Stykel
This paper presents a novel Riemannian conjugate gradient method for the Kohn-Sham energy minimization problem in density functional theory (DFT), with a focus on non-metallic crys…
Hierarchical Rank-One Sequence Convexification for the Relaxation of Variational Problems with Microstructures
Maximilian Köhler, Timo Neumeier, Malte. A. Peter +2
This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one seque…