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

10 June 2021

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

4 / 4 papers shown

Title
MD-NOMAD: Mixture density nonlinear manifold decoder for emulating stochastic differential equations and uncertainty propagation Akshay Thakur Souvik Chakraborty 34 1 0 24 Apr 2024
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 30 2 0 25 Sep 2023
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations Simon Koop M. Peletier J. Portegies Vlado Menkovski DiffM 22 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 47 16 0 31 Jan 2022