Sebastian Kaltenbach

According to our database, Sebastian Kaltenbach authored at least 10 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie-Christin Metz, Sergey Litvinov, Sebastian Kaltenbach, Leonhard F. Feiner, Laurin Lux, Florian Kofler, Jana Lipková, Jonas Latz, Daniel Rueckert, Bjoern H. Menze, Benedikt Wiestler

CoRR, 2024

Generative Learning for Forecasting the Dynamics of Complex Systems.

Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos

CoRR, 2024

Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement Learning.

Jan-Philipp von Bassewitz, Sebastian Kaltenbach, Petros Koumoutsakos

CoRR, 2024

2023

Physics-aware, probabilistic machine learning in the Small Data regime.

Sebastian Kaltenbach

PhD thesis, 2023

Interpretable learning of effective dynamics for multiscale systems.

Emmanuel Menier, Sebastian Kaltenbach, Mouadh Yagoubi, Marc Schoenauer, Petros Koumoutsakos

CoRR, 2023

Interpretable reduced-order modeling with time-scale separation.

Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos

CoRR, 2023

2022

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems.

Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis

CoRR, 2022

2021

Physics-enhanced Neural Networks in the Small Data Regime.

Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

CoRR, 2021

Physics-aware, probabilistic model order reduction with guaranteed stability.

Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems.

Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis

J. Comput. Phys., 2020