Demonstrates efficient adaptive sampling in solving partial differential equations, indicating improved outcomes for physics-informed neural networks.
The residual-based adversarial-gradient moving sample (RAMS) method steers training samples along adversarial gradients toward residual peaks of partial differential equations (PDEs), providing efficient adaptive sampling for physics-informed neural networks and neural operators in solving PDEs. More details can be found in the Research Article by Lu Lu and co-workers (DOI: 10.1002/aidi.202500214).
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Ouyang et al. (2026) studied this question.
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