We present a simple yet powerful framework for solving inverse problems by leveraging automatic differentiation. Our method is broadly applicable whenever a smooth cost function can be defined near the true solution, and a numerical simulator is available. As a concrete example, we demonstrate that our method can accurately reconstruct the spatial profiles in a conductor from magnetotransport measurements. Even if the given data are insufficient to uniquely determine the profiles, the same framework enables effective reverse modeling. This method is general, flexible, and readily adaptable to a broad class of inverse problems across condensed matter physics and beyond.
@article{arxiv.2506.13210,
title = {Backsolution: A Framework for Solving Inverse Problems via Automatic Differentiation},
author = {Koji Kobayashi and Tomi Ohtsuki},
journal= {arXiv preprint arXiv:2506.13210},
year = {2025}
}