Physics-Informed Neural Operator for Learning Partial Differential Equations. (arXiv:2111.03794v3 [cs.LG] UPDATED)

In this paper, we propose physics-informed neural operators (PINO) that uses
available data and/or physics constraints to learn the solution operator of a
family of parametric Partial Differential Equation (PDE). This hybrid approach
allows PINO to overcome the limitations of purely data-driven and physics-based
methods. For instance, data-driven methods fail to learn when data is of
limited quantity and/or quality, and physics-based approaches fail to optimize
on challenging PDE constraints. By combining both data and PDE constraints,
PINO overcomes all …

arxiv challenges constraints data data-driven differential equation equation family hybrid hybrid approach learn operators paper parametric physics physics-informed property quality solution