Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph