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Sun, Jingtong and Berner, Julius and Richter, Lorenz and Zeinhofer, Marius and Müller, Johannes and Azizzadenesheli, Kamyar and Anandkumar, Anima (2024) Dynamical Measure Transport and Neural PDE Solvers for Sampling. Preprint arXiv . (Unpublished)

Winkler, Ludwig and Richter, Lorenz and Opper, Manfred (2024) Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models. Preprint arXiv . (Unpublished)

Berner, Julius and Richter, Lorenz and Ullrich, Karen (2024) An optimal control perspective on diffusion-based generative modeling. Preprint arXiv . (Unpublished)

Borrell, Enric Ribera and Quer, Jannes and Richter, Lorenz and Schütte, Christof (2023) Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics. Preprint to appear in SISC 2024 . (In Press)

Richter, Lorenz and Sallandt, Leon and Nüsken, Nikolas (2023) From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. Preprint . (Unpublished)

Richter, Lorenz and Berner, Julius and Liu, Guang-Horng (2023) Improved sampling via learned diffusions. Preprint . (Unpublished)

Nüsken, Nikolas and Richter, Lorenz (2023) Interpolating Between BSDEs and PINNs: Deep Learning for Elliptic and Parabolic Boundary Value Problems. Journal of Machine Learning, 2 . pp. 31-64.

Richter, Lorenz and Berner, Julius (2022) Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning. Preprint arXiv . (Unpublished)

Becker, Simon and Hartmann, Carsten and Redmann, Martin and Richter, Lorenz (2022) Error bounds for model reduction of feedback-controlled linear stochastic dynamics on Hilbert spaces. Stochastic Processes and their Applications, 149 . pp. 107-141.

Nüsken, Nikolas and Richter, Lorenz (2021) Solving high-dimensional Hamilton–Jacobi–Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space. Partial Differential Equations and Applications, 2 (48). pp. 1-48.

Richter, Lorenz and Sallandt, Leon and Nüsken, Nikolas (2021) Solving high-dimensional parabolic PDEs using the tensor train format. Proceedings of the 38th International Conferenceon Machine Learning, 139 . pp. 8998-9009.

Richter, Lorenz and Boustati, Ayman and Nüsken, Nikolas and Ruiz, Francisco J. R. and Akyildiz, Ömer Deniz (2020) VarGrad: A Low-Variance Gradient Estimator for Variational Inference. Advances in Neural Information Processing Systems 2020 . pp. 1-25. (Submitted)

Becker, Simon and Richter, Lorenz (2020) Model order reduction for (stochastic-) delay equations with error bounds. arXive . pp. 1-23. (Submitted)

Becker, Simon and Hartmann, Carsten and Redmann, Martin and Richter, Lorenz (2019) Feedback control theory & Model order reduction for stochastic equations. ArXive . pp. 1-36. (Submitted)

Hartmann, Carsten and Kebiri, Omar and Neureither, Lara and Richter, Lorenz (2019) Variational approach to rare event simulation using least-squares regression. Chaos, 29 (6). ISSN 1054-1500 (print); 1089-7682 (online)