4 papers
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…
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 fundamenta…
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…