Publications
Preprints
Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
Gruhlke, R., Berner J., Richter L., D. Sommer.
ICML 2026, submitted
Multiplicative Diffusion Models: Beyond Gaussian Latents
Gruhlke, R., Resseguier, V., Talla Makougne, M.
Accepted for publication in ICLR 2026
Provable Mixed-Noise Learning with Flow-Matching
Hagemann, P., Gruhlke, R., Stankewitz, B., Schillings, C., & Steidl, G.
Accepted for publication in Theoretical Foundations of Deep Learning, book series, Springer Nature
arXiv preprint, arXiv:2508.18122 (2025)
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems
Gruhlke, R., Hanu, M., Schillings, C., & Wacker, P.
Accepted for publication in SIAM/ASA Journal on Uncertainty Quantification
arXiv preprint, arXiv:2508.18122. (2025)
Optimal sampling for stochastic and natural gradient descent
Gruhlke, R., Nouy, A., & Trunschke, P.
arXiv preprint, arXiv:2402.03113 (2024) — In revision at JOTA
Generative modeling with low-rank Wasserstein polynomial chaos expansions
Gruhlke, R., & Eigel, M.
arXiv preprint, arXiv:2203.09358v2 (2024)
Upcoming Preprints
The following preprints will be available soon, reflecting current research directions:
-
"Automatic Differentiation within Hierarchical Tensor Formats"
with D. Moser
-
"Quasi-Monte Carlo Meets Kernel Cubature through Optimal Sampling"
with V. Kaarnioja & C. Schillings
-
"High-Accuracy Physics-Informed Machine Learning with Optimal Sampling and Tensor Networks"
with P. Trunschke
Journal/Conference Papers
Reverse Diffusion Sampling with Tensor Train Approximations of Hamilton–Jacobi–Bellman Equations
Gruhlke, R., Sommer, D., Kirstein, M., Eigel, M., & Schillings, C.
SIAM Journal on Scientific Computing, 48(1), C103-C135.
Importance Corrected Neural JKO Sampling
Hertrich, J. & Gruhlke, R. (to appear)
International Conference on Machine Learning (ICML), Accepted (2025)
Multiplicative score-based generative models for fluid dynamics
Talla, M., Resseguier, V., Gruhlke, R., Heitz, D., & Mémin, E.
SIAM Computational Science and Engineering (CSE25) (2025)
Less Interaction with Forward Models in Langevin Dynamics: Enrichment and Homotopy
Eigel, M., Gruhlke, R. & Sommer, D.
SIAM Journal on Applied Dynamical Systems, 23(3): 1870–1908 (2024)
Numerical upscaling of parametric microstructures in a possibilistic uncertainty framework with tensor trains
Eigel, M., Gruhlke, R., Moser, D., & Grasedyck, L.
Computational Mechanics, 71(4): 615–636 (2023)
Local surrogate responses in the Schwarz alternating method for elastic problems on random voided domains
Drieschner, M., Gruhlke, R., Petryna, Y., Eigel, M., & Hömberg, D.
Computer Methods in Applied Mechanics and Engineering, 405: 115858 (2023)
Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion
Eigel, M., Gruhlke, R., & Marschall, M.
Statistics and Computing, 32(2): 27 (2022)
A local hybrid surrogate‐based finite element tearing interconnecting dual‐primal method for nonsmooth random partial differential equations
Eigel, M. & Gruhlke, R.
International Journal for Numerical Methods in Engineering, 122(4): 1001–1030 (2021)
Comparison of various uncertainty models with experimental investigations regarding the failure of plates with holes
Drieschner, M., Eigel, M., Gruhlke, R., Hömberg, D. & Petryna, Y.
Reliability Engineering & System Safety, 203: 107106 (2020)
Challenges of order reduction techniques for problems involving polymorphic uncertainty
Pivovarov, D., et al.
GAMM‐Mitteilungen, 42(2): e201900011 (2019)
Assessment and design of an engineering structure with polymorphic uncertainty quantification
Papaioannou, I., et al.
GAMM‐Mitteilungen, 42(2): e201900009 (2019)
