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20242026
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math.NA2026

Iterative thresholding low-rank time integration for high-dimensional problems

Markus Bachmayr, Tianyu Jin, Polina Sachsenmaier +1

This work analyzes a method for time integration of high-dimensional linear Schrödinger-type problems based on hierarchical tensor approximations. In particular, this method provid…

math.NA2026

Low-rank eigenvalue solvers for block-sparse matrix product states

Markus Bachmayr, Sebastian Krämer, Max Pfeffer

We consider an iterative eigensolver for Schrödinger equations that constructs low-rank approximations of eigenfunctions with accuracy-adapted ranks, with particular focus on ferm…

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.NA2025

An adaptive space-time method for nonlinear poroviscoelastic flows with discontinuous porosities

Markus Bachmayr, Simon Boisserée

This paper is concerned with a space-time adaptive numerical method for instationary porous media flows with nonlinear interaction between porosity and pressure, with focus on prob…

math.NA2025

Iterative thresholding low-rank time integration

Markus Bachmayr, Matthieu Dolbeault, Polina Sachsenmaier

We develop time integration methods in low-rank representation that can adaptively adjust approximation ranks to achieve a prescribed accuracy, while ensuring that these ranks rema…

math.NA2025

Sparse and low-rank approximations of parametric elliptic PDEs: the best of both worlds

Markus Bachmayr, Huqing Yang

A new approximation format for solutions of partial differential equations depending on infinitely many parameters is introduced. By combining low-rank tensor approximation in a se…