Sebastian Kaltenbach

CoRR, 2024

A Learnable Prior Improves Inverse Tumor Growth Modeling.

[BibT_eX]

[DOI]

CoRR, 2024

Generative Learning for Forecasting the Dynamics of Complex Systems.

[BibT_eX]

[DOI]

Han Gao

Jan-Philipp von Bassewitz

CoRR, 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

2023

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

[BibT_eX]

[DOI]

PhD thesis, 2023

Interpretable learning of effective dynamics for multiscale systems.

[BibT_eX]

[DOI]

CoRR, 2023

Interpretable reduced-order modeling with time-scale separation.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems.

[BibT_eX]

[DOI]

Paris Perdikaris

CoRR, 2022

2021

Physics-enhanced Neural Networks in the Small Data Regime.

[BibT_eX]

[DOI]

Jonas Eichelsdörfer

CoRR, 2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]