167 citations · 426 across the 14 of their papers we have counts for
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physics.comp-ph2019★ 72 cited
Constraint-Aware Neural Networks for Riemann Problems
Jim Magiera, Deep Ray, Jan S. Hesthaven +1
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates. In many applications physical…
physics.comp-ph2019★ 167 cited
Deep learning observables in computational fluid dynamics
Kjetil O. Lye, Siddhartha Mishra, Deep Ray
Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered…