Sebastian Kaltenbach

According to our database, Sebastian Kaltenbach authored at least 18 papers between 2007 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming.

Vasileios Saketos, Sebastian Kaltenbach, Sergey Litvinov, Petros Koumoutsakos

CoRR, August, 2025

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data.

Lothar Heimbach, Sebastian Kaltenbach, Petr Karnakov, Francis J. Alexander, Petros Koumoutsakos

CoRR, May, 2025

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning.

Paul Fischer, Sebastian Kaltenbach, Sergey Litvinov, Sauro Succi, Petros Koumoutsakos

CoRR, April, 2025

Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling.

Michal Balcerak, Tamaz Amiranashvili, Suprosanna Shit, Antonio Terpin, Sebastian Kaltenbach, Petros Koumoutsakos, Bjoern H. Menze

CoRR, April, 2025

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

IEEE Trans. Medical Imaging, March, 2025

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows.

Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos

Proceedings of the Conference on Parsimony and Learning, 2025

Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning.

Jan-Philipp von Bassewitz, Sebastian Kaltenbach, Petros Koumoutsakos

Proceedings of the Conference on Parsimony and Learning, 2025

2024

Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations.

Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos

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

2007

Biphasic Optical Signal of an oscillating nonstationary Belousov-zhabotinsky Bulk Reaction and its Similarity to Some neurophysiological Events.

Kerstin Dolzmann, Sebastian Kaltenbach, Wolfgang Hanke, Vera Maura Fernandes de Lima

Int. J. Bifurc. Chaos, 2007