1 research outputs found
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
We 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) [Raissi et al., J. Comput. Phys.,
378:686--707, 2019] 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 existing
methods in terms of the standard L2-norm metric.Comment: Accepted by AAAI 202