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Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
Natural Gradient Descent (NGD) has emerged as a promising optimization algorithm for training neural network-based solvers for partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs). However, its practical use is often limited by the high computational cost of solvin
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