Deep reinforcement learning for adaptive mesh refinement

Deep reinforcement learning for adaptive mesh refinement

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 o...

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Bibliographic Details
Main Authors: Foucart, Corbin, Charous, Aaron, Lermusiaux, Pierre F.J.
Format: Article
Language:English
Published: Elsevier BV 2024
Subjects:
Computer Science Applications
Physics and Astronomy (miscellaneous)
Applied Mathematics
Computational Mathematics
Modeling and Simulation
Numerical Analysis
Online Access:https://hdl.handle.net/1721.1/153763

Internet

https://hdl.handle.net/1721.1/153763

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