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Partial Differential Equations Solving
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
Published:4/17/2025
Reinforcement Learning-Driven Adaptive SamplingPhysics-Informed Neural NetworksPartial Differential Equations SolvingSingle-Round Sampling TrainingMarkov Decision Process
This paper introduces RLPINNs, a reinforcement learningdriven adaptive sampling framework that enhances the training efficiency of PhysicsInformed Neural Networks (PINNs) by enabling optimal training point selection in a single round of sampling, significantly improving accura
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HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions
Published:9/5/2025
Multi-Physics Neural OperatorsZero-Shot GeneralizationSwin Transformer HypernetworkPhysics-Informed Neural NetworksPartial Differential Equations Solving
HyPINO is introduced as a multiphysics neural operator for zeroshot generalization across various PDEs without taskspecific finetuning, combining a Swin Transformer hypernetwork and mixed supervision for improved accuracy in benchmarks.
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