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Learning When to Refine: Long-Horizon Reinforcement Learning for Budgeted Neural-Operator PDE Solvers
Author1, Author2, Author3, Author4, Author5
reinforcement learningneural operatorsPDE solvingadaptive methods
2610.06883
Builder Relevance
1h ago70%
Abstract
This paper presents a method for optimizing refinement allocation in neural operator PDE solvers under a budget constraint.
Reality Card
Core Claim
The proposed rollout-verified policy improvement (RV-PI) method significantly reduces trajectory error in PDE solving while adhering to a fixed refinement budget.
Method / Result
RV-PI achieved a mean trajectory relative L2 error of 0.6910 on the shallow-water benchmark, improving over previous methods by 5.37%.
Limitations
The method's performance may vary based on the specific benchmarks and the chosen refinement budget, which could affect generalizability.
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