Partial Differential Equations (PDE) lay the foundation for modeling a wide variety of scientific phenomena. Traditional solvers tend to be slow when high-fidelity solutions are needed. We introduce neural-operator, a data-driven approach that aims to directly learn the solution operator of PDEs. Unlike neural networks that learn function mapping between finite-dimensional spaces, neural operator extends that to learning the operator between infinite-dimensional spaces. This makes the neural operator independent of resolution and grid of training data and allows for zero-shot generalization to higher resolution evaluations. We find that the neural operator is able to solve the Navier-Stokes equation in the turbulent regime with a 1000x speedup compared to traditional methods.

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