35 citations · 165 across the 18 of their papers we have counts for
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cs.LG2022★ 4 cited
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Ketan Mittal, Tarik Dzanic +5
Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need…
cs.LG2021★ 7 cited
Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Tarik Dzanic, Brenden Petersen +9
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate c…