DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
Keywords:(Deep) Neural Network Algorithms
AbstractWe present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in extrapolating solutions in time beyond the range of temporal domain used in training. Our choice for a baseline method is physics-informed neural network (PINN) because the method parameterizes not only the solutions, but also the equations that describe the dynamics of physical processes. We demonstrate that PINN performs poorly on extrapolation tasks in many benchmark problems. To address this, we propose a novel method for better training PINN and demonstrate that our newly enhanced PINNs can accurately extrapolate solutions in time. Our method shows up to 72% smaller errors than state-of-the-art methods in terms of the standard L2-norm metric.
How to Cite
Kim, J., Lee, K., Lee, D., Jhin, S. Y., & Park, N. (2021). DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 8146-8154. https://doi.org/10.1609/aaai.v35i9.16992
AAAI Technical Track on Machine Learning II