Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.

ABSTRACT: In recent years, advances in computing hardware and computational methods have prompted a wealth of activities for solving inverse problems in physics. These problems are often described by systems of partial differential equations (PDEs). The advent of machine learning has reinvigorated the interest in solving inverse problems using neural networks (NNs). In these efforts, the solution of the PDEs is expressed as NNs trained through the minimization of a loss function involving the PDE. Here, we show how to accelerate this approach by five orders of magnitude by deploying, instead of NNs, conventional PDE approximations. The framework of optimizing a discrete loss (ODIL) minimizes a cost function for discrete approximations of the PDEs using gradient-based and Newton's methods. The framew

SUBMITTER: Karnakov P 

PROVIDER: S-EPMC10799659 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature