A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training
This addresses a scalability problem for researchers using PINNs in high-dimensional physics simulations, though it appears incremental as it builds on existing curriculum training ideas.
The paper tackled the issue of inefficient collocation point distribution in Physics-Informed Neural Networks (PINNs) by proposing a curriculum-training-based method, resulting in a significant decrease in training time and enhanced solution quality for a two-dimensional magnetohydrodynamic reconstruction task.
Physics-informed Neural Networks (PINNs) often have, in their loss functions, terms based on physical equations and derivatives. In order to evaluate these terms, the output solution is sampled using a distribution of collocation points. However, density-based strategies, in which the number of collocation points over the domain increases throughout the training period, do not scale well to multiple spatial dimensions. To remedy this issue, we present here a curriculum-training-based method for lightweight collocation point distributions during network training. We apply this method to a PINN which recovers a full two-dimensional magnetohydrodynamic (MHD) solution from a partial sample taken from a baseline MHD simulation. We find that the curriculum collocation point strategy leads to a significant decrease in training time and simultaneously enhances the quality of the reconstructed solution.