OpenAlex
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
Work
Year: 2020
Type: article
Abstract: We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scatter... more
Source: SIAM Journal on Scientific Computing
Authors Liu Yang, Dongkun Zhang, George Em Karniadakis
Institution Brown University
Cites: 13
Cited by: 297
Related to: 10
FWCI: 20.26
Citation percentile (by year/subfield): 99.99
Topic: Model Reduction and Neural Networks
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Sustainable Development Goal Reduced inequalities
Open Access status: green
Funders U.S. Department of Energy, U.S. Department of Energy, Army Research Office
Grant IDS DE-SC0019453, DE-SC0019434, W911NF-18-1-0301