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This paper examines the coincidence of neural networks with numerical methods for solving spatiotemporal physical problems.
A multi-pass GAN for fluid flow super-resolution
Maximilian Werhahn, You Xie, Mengyu Chu, and Nils Thuerey · 1906
Earlier work this paper cites.
A critique of pure learning and what artificial neural networks can learn from animal brains
Anthony M. Zador · 2041
Cited alongside, same era.
Data-driven discretization: a method for systematic coarse graining of partial differential equations
Yohai Bar-Sinai, Stephan Hoyer, Jason Hickey, and Michael P Brenner
Cited in the paper.
Multi-level residual networks from dynamical systems view
Bo Chang, Lili Meng, Eldad Haber, Frederick Tung, and David Begert
Cited in the paper.
Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud
Cited in the paper.
A difference method for numerical calculation of discontinuous solutions of the equations of hydrodynamics
Sergei Konstantinovich Godunov
Cited in the paper.
Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems , volume 98
Randall J LeVeque
Cited in the paper.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky
Cited in the paper.
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
Pantelis R Vlachas, Wonmin Byeon, Zhong Y Wan, Themistoklis P Sapsis, and Petros Koumoutsakos
Cited in the paper.
Physics-informed generative learning to emulate unresolved physics in climate models
J. Wu, K. Kashinath, A. Albert, M. Prabhat, and H. Xiao
Cited in the paper.
tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey
Cited in the paper.
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