Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence. - Anna Andrle; Nando Farchmin; Paul Hagemann; Sebastian Heidenreich; Victor Soltwisch; Gabriele Steidl (SSVM, 2021)

Efficient approximation of high-dimensional exponentials by tensornetworks. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (CoRR, 2021)

Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (CoRR, 2021)

Adaptive Nonintrusive Reconstruction of Solutions to High-Dimensional Parametric PDEs. - Martin Eigel; Nando Farchmin; Sebastian Heidenreich; Philipp Trunschke (SIAM J. Sci. Comput., 2023)

PyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansions. - Nando Hegemann; Sebastian Heidenreich (J. Open Source Softw., 2023)

Mixed noise and posterior estimation with conditional deepGEM. - Paul Hagemann; Johannes Hertrich; Maren Casfor; Sebastian Heidenreich; Gabriele Steidl (Mach. Learn. Sci. Technol., 2024)