Deep Reinforcement Learning for Adaptive Mesh Refinement

Understand

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

• However, these spatial refinement strategies are often heuristic and rely on domain-specific knowledge or trial-and-error.
• We treat the process of adaptive mesh refinement as a local, sequential decision-making problem under incomplete information, formulating AMR as a partially observable Markov decision process.
• Using a deep reinforcement learning approach, we train policy networks for AMR strategy directly from numerical simulation.