Physics-Informed Neural Operator for Learning Partial Differential Equations

In this article, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric Partial Differential Equations (PDE). PINO is the first hybrid approach incorporating data and PDE constraints at different resolutions to learn the operator. Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution. The resulting PINO model can accurately approximate the ground-truth solution operator for many popular PDE families and shows no degradation in accuracy even under zero-shot super-resolution, that is, being able to predict beyond the resolution of training…

• CI
  California Institute of Technology
• N
  Nvidia
• RS
  Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology