Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability

9 June 2019

Papers citing "Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability"

10 / 10 papers shown

Title
Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations Yue Li L. Zhang AI4CE 23 1 0 13 Apr 2025
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation Chuanqi Chen Nan Chen Yinling Zhang Jin-Long Wu AI4CE 30 2 0 26 Oct 2024
GIT-Net: Generalized Integral Transform for Operator Learning Chao Wang Alexandre H. Thiery AI4CE 11 0 0 05 Dec 2023
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder Priyam Gupta Peter J. Schmid D. Sipp T. Sayadi Georgios Rigas 14 4 0 16 Oct 2023
Invariant preservation in machine learned PDE solvers via error correction N. McGreivy Ammar Hakim AI4CE PINN 21 8 0 28 Mar 2023
KoopmanLab: machine learning for solving complex physics equations Wei Xiong Muyuan Ma Xiaomeng Huang Ziyang Zhang Pei Sun Yang Tian AI4CE 19 13 0 03 Jan 2023
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 26 136 0 26 May 2022
Diffeomorphically Learning Stable Koopman Operators Petar Bevanda Maximilian Beier Sebastian Kerz Armin Lederer Stefan Sosnowski Sandra Hirche 11 21 0 08 Dec 2021
A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic model A. Tavasoli T. Henry Heman Shakeri 23 3 0 01 Aug 2021
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 109 355 0 30 Oct 2017