Treating training samples as trainable parameters and moving them along the residual's adversarial gradient improves accuracy across PINN and operator learning benchmarks.
Artifi- cial intelligence for partial differential equations in computational mechanics: A review
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RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
Treating training samples as trainable parameters and moving them along the residual's adversarial gradient improves accuracy across PINN and operator learning benchmarks.