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Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs). However, they face challenges related to spectral bias (the tendency to learn low-frequency components while struggling with high-frequency features) and unstable converge
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