most citedDeep Reinforcement Learning for Adaptive Mesh Refinement

2 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

math.NA2022★ 1 cited

Stable rank-adaptive Dynamically Orthogonal Runge-Kutta schemes

Aaron Charous, Pierre F. J. Lermusiaux

We develop two new sets of stable, rank-adaptive Dynamically Orthogonal Runge-Kutta (DORK) schemes that capture the high-order curvature of the nonlinear low-rank manifold. The DOR…

cs.CE2022★ 2 cited

Deep Reinforcement Learning for Adaptive Mesh Refinement

Corbin Foucart, Aaron Charous, Pierre F. J. Lermusiaux

Finite element discretizations of problems in computational physics often rely on adaptive mesh refinement (AMR) to preferentially resolve regions containing important features dur…