Learning Specialized Activation Functions for Physics-informed Neural Networks

Understand

Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty.

• In this work, we reveal the connection between the optimization difficulty of PINNs and activation functions.
• Specifically, we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties.
• Existing works usually choose activation functions by inefficient trial-and-error.