Learning invariant representations of time-homogeneous stochastic
dynamical systems

Learning invariant representations of time-homogeneous stochastic dynamical systems

19 July 2023

Vladimir Kostic

Riccardo Grazzi

Massimiliano Pontil

Papers citing "Learning invariant representations of time-homogeneous stochastic dynamical systems"

9 / 9 papers shown

Title
Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning Ruikun Li Huandong Wang Qingmin Liao Yong Li 41 0 0 24 Feb 2025
Koopman-Equivariant Gaussian Processes Petar Bevanda Max Beier Armin Lederer A. Capone Stefan Sosnowski Sandra Hirche AI4TS 63 1 0 10 Feb 2025
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne P. Novelli Massimiliano Pontil 21 0 0 18 Oct 2024
Data-Driven Optimal Feedback Laws via Kernel Mean Embeddings Petar Bevanda Nicolas Hoischen Stefan Sosnowski Sandra Hirche Boris Houska 18 2 0 23 Jul 2024
Neural Conditional Probability for Inference Vladimir Kostic Karim Lounici Grégoire Pacreau P. Novelli G. Turri Massimiliano Pontil UQCV TPM 28 0 0 01 Jul 2024
Learning the Infinitesimal Generator of Stochastic Diffusion Processes Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne Massimiliano Pontil DiffM 19 4 0 21 May 2024
Neural Koopman prior for data assimilation Anthony Frion Lucas Drumetz M. Dalla Mura Guillaume Tochon Abdeldjalil Aissa El Bey AI4TS AI4CE 20 4 0 11 Sep 2023
Learning Stable Koopman Embeddings Fletcher Fan Bowen Yi D. Rye Guodong Shi I. Manchester 25 33 0 13 Oct 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 221 498 0 20 Oct 2020