1 code implementation • 13 Feb 2023 • Shawn G. Rosofsky, E. A. Huerta
Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations.
1 code implementation • 23 Mar 2022 • Shawn G. Rosofsky, Hani Al Majed, E. A. Huerta
We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena.
