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) 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.
|Title of host publication||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Publisher||Association for the Advancement of Artificial Intelligence|
|Number of pages||9|
|Publication status||Published - 2021|
|Event||35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online|
Duration: 2021 Feb 2 → 2021 Feb 9
|Name||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Conference||35th AAAI Conference on Artificial Intelligence, AAAI 2021|
|Period||21/2/2 → 21/2/9|
Bibliographical noteFunding Information:
Noseong Park (firstname.lastname@example.org) is the corresponding author. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, a wholly owned subsidiary of Honeywell International, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence