A secant-based adaptive correction augments first-order optimizers to improve convergence speed, stability, and accuracy when training PINNs on challenging PDEs.
ASAM: Adaptive sharpness-aware mini- mization for scale-invariant learning of deep neural networks
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Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
A secant-based adaptive correction augments first-order optimizers to improve convergence speed, stability, and accuracy when training PINNs on challenging PDEs.