3 papers
math.NA2026
Preconditioning and Numerical Stability in Neural Network Training for Parametric PDEs
Markus Bachmayr, Wolfgang Dahmen, Chenguang Duan +1
In the context of training neural network-based approximations of solutions of parameter-dependent PDEs, we investigate the effect of preconditioning via well-conditioned frame rep…
math.OC2026
Anderson Mixing in Bures Wasserstein Space of Gaussian Measures
Vitalii Aksenov, Martin Eigel, Mathias Oster
Various statistical tasks, including sampling or computing Wasserstein barycenters, can be reformulated as fixed-point problems for operators on probability distributions. Accelera…
physics.chem-ph2025
Neural network approximation of regularized density functionals
Mihály A. Csirik, Andre Laestadius, Mathias Oster
Density functional theory is one of the most efficient and widely used computational methods of quantum mechanics, especially in fields such as solid state physics and quantum chem…