Generative adversarial networks for PDE learning
May 15, 2017 — August 25, 2020
calculus
dynamical systems
geometry
Hilbert space
how do science
Lévy processes
machine learning
neural nets
PDEs
physics
regression
sciml
SDEs
signal processing
statistics
statmech
stochastic processes
surrogate
time series
uncertainty
Suspiciously similar content
GANs for PDE learning.
(Bao et al. 2020; Yang, Zhang, and Karniadakis 2020; Zang et al. 2020).
A recent example from fluid-flow dynamics (Chu et al. 2021) has particularly beautiful animations:
1 References
Bao, Ye, Zang, et al. 2020. “Numerical Solution of Inverse Problems by Weak Adversarial Networks.” Inverse Problems.
Chu, Thuerey, Seidel, et al. 2021. “Learning Meaningful Controls for Fluids.” ACM Transactions on Graphics.
Yang, Zhang, and Karniadakis. 2020. “Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations.” SIAM Journal on Scientific Computing.
Zang, Bao, Ye, et al. 2020. “Weak Adversarial Networks for High-Dimensional Partial Differential Equations.” Journal of Computational Physics.