A Novel Automatic Mixed Precision Approach For Physics Informed Training

Physics Informed Neural Networks (PINNs) allow for a clean way of training models directly using physical governing equations. Training PINNs requires higher-order derivatives that typical data driven training does not require and increases training costs. In this work, we address the performance challenges of training PINNs by developing a new automatic mixed precision approach for physics informed training. This approach uses a derivative scaling strategy that enables the Automatic Mixed Precision (AMP) training for PINNs without running into training instabilities that the regular AMP approach encounters.