Learning effective stochastic differential equations from microscopic
simulations: linking stochastic numerics to deep learning

Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning

10 June 2021

George A. Kevrekidis

Tom S. Bertalan

Sebastian Reich

Ioannis G. Kevrekidis

Papers citing "Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning"

19 / 19 papers shown

Title
A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown Lévy Process Dynamics Qihao Ye Xiaochuan Tian Yuhua Zhu 36 1 0 02 Apr 2025
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 80 0 0 30 Mar 2025
Non-parametric Inference for Diffusion Processes: A Computational Approach via Bayesian Inversion for PDEs Maximilian Kruse Sebastian Krumscheid 26 0 0 04 Nov 2024
Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems Yuán Chen Dongbin Xiu 34 0 0 27 Aug 2024
Feynman-Kac Operator Expectation Estimator Jingyuan Li Wei Liu 38 0 0 02 Jul 2024
RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators Gianluca Fabiani Ioannis G. Kevrekidis Constantinos Siettos A. Yannacopoulos 27 11 0 08 Jun 2024
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation Akshay Thakur Souvik Chakraborty 42 1 0 24 Apr 2024
Stochastic parameter reduced-order model based on hybrid machine learning approaches Cheng Fang Jinqiao Duan 25 0 0 24 Mar 2024
From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE Samira Pakravan Nikolaos Evangelou Maxime Usdin Logan Brooks James Lu 26 0 0 05 Mar 2024
DynGMA: a robust approach for learning stochastic differential equations from data Aiqing Zhu Qianxiao Li OOD DiffM 25 3 0 22 Feb 2024
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling N. Evangelou Tianqi Cui J. M. Bello-Rivas Alexei Makeev Ioannis G. Kevrekidis 32 1 0 01 Nov 2023
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points Gianluca Fabiani N. Evangelou Tianqi Cui J. M. Bello-Rivas Cristina P. Martin-Linares Constantinos Siettos Ioannis G. Kevrekidis 38 2 0 25 Sep 2023
Early warning indicators via latent stochastic dynamical systems Lingyu Feng Ting Gao Wang Xiao Jinqiao Duan 15 2 0 07 Sep 2023
Constructing Custom Thermodynamics Using Deep Learning Xiaoli Chen Beatrice W. Soh Z. Ooi E. Vissol-Gaudin Haijun Yu K. Novoselov K. Hippalgaonkar Qianxiao Li AI4CE 11 7 0 08 Aug 2023
Learning Stochastic Dynamical System via Flow Map Operator Yuán Chen D. Xiu AI4CE 27 15 0 05 May 2023
Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems Cheng Fang Yubin Lu Ting Gao Jinqiao Duan 33 4 0 01 May 2023
Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information S. Malani Tom S. Bertalan Tianqi Cui J. Avalos Michael Betenbaugh Ioannis G. Kevrekidis PINN AI4CE 37 4 0 27 Apr 2023
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations Simon Koop M. Peletier J. Portegies Vlado Menkovski DiffM 35 1 0 17 Nov 2022
An end-to-end deep learning approach for extracting stochastic dynamical systems with $α$ -stable Lévy noise Cheng Fang Yubin Lu Ting Gao Jinqiao Duan 55 16 0 31 Jan 2022