ResearchTrend.AI
Computing non-equilibrium trajectories by a deep learning approach

Computing non-equilibrium trajectories by a deep learning approach

E. Simonnet
ArXivPDFHTML

Papers citing "Computing non-equilibrium trajectories by a deep learning approach"

3 / 3 papers shown
Title
Scalable Methods for Computing Sharp Extreme Event Probabilities in Infinite-Dimensional Stochastic Systems
Scalable Methods for Computing Sharp Extreme Event Probabilities in Infinite-Dimensional Stochastic Systems
Timo Schorlepp
Shanyin Tong
T. Grafke
G. Stadler
37
7
0
21 Mar 2023
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Benjamin Moseley
Andrew Markham
T. Nissen‐Meyer
PINN
48
211
0
16 Jul 2021
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
Ziqi Liu
Wei Cai
Zhi-Qin John Xu
AI4CE
297
122
0
22 Jul 2020