Evolutionary algorithms can efficiently find good hyperparameter configurations for PINNs by combining fast screening of many candidates with full training of the best ones, avoiding the manual tuning and convergence issues that plague standard approaches.
This paper tackles the challenge of tuning Physics-Informed Neural Networks (PINNs) by proposing a two-stage evolutionary algorithm approach. Instead of manually searching for good hyperparameters, the method first quickly screens many configurations using short training runs, then fully trains the most promising ones.