SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning
Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform replacement for existing optimizers.
Guang-Yuan Wang, Mads Toftrup, Sebastian Loeschcke et al.
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