Learnable Activation Functions in Physics-Informed Neural Networks for
Solving Partial Differential Equations
Learnable Activation Functions in Physics-Informed Neural Networks for
Solving Partial Differential Equations
We investigate the use of learnable activation functions in Physics-Informed Neural Networks (PINNs) for solving Partial Differential Equations (PDEs). Specifically, we compare the efficacy of traditional Multilayer Perceptrons (MLPs) with fixed and learnable activations against Kolmogorov-Arnold Networks (KANs), which employ learnable basis functions. Physics-informed neural networks (PINNs) have emerged as …