no code implementations • 29 Sep 2023 • Kai Weixian Lan, Elias Gueidon, Ayano Kaneda, Julian Panetta, Joseph Teran
The core of our solver is a neural network trained to approximate the inverse of a discrete structured-grid Laplace operator for a domain of arbitrary shape and with mixed boundary conditions.
no code implementations • 22 May 2022 • Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Kala, David Hyde, Joseph Teran
We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations.
