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cs.LG2022★ 3 cited
A composable machine-learning approach for steady-state simulations on high-resolution grids
Rishikesh Ranade, Chris Hill, Lalit Ghule +1
In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and…
cs.LG2022★ 7 cited
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers
Björn Lütjens, Catherine H. Crawford, Campbell D Watson +2
Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution. Reduced-o…