Using machine learning hardware to solve linear partial differential equations with finite difference methods
This study explores the potential of utilizing hardware built for Machine Learning (ML) tasks as a platform for solving linear Partial Differential Equations via numerical methods. We examine the feasibility, benefits, and obstacles associated with this approach. Given an Initial Boundary Value Problem (IBVP) and a finite difference method, we directly compute stencil coefficients and assign them
