STEP: extraction of underlying physics with robust machine learning

• Published in: Royal Society open science Vol. 11; no. 5; pp. 231374 - 12
• Main Authors: Alaa El-Din, Karim K, Forte, Alessandro, Kasim, Muhammad Firmansyah, Miniati, Francesco, Vinko, Sam M
• Format: Journal Article
• Language: English
• Published: England The Royal Society Publishing 01.06.2024; The Royal Society
• Subjects: Artificial intelligence; differentiable modelling; Inverse problems; Machine learning; Neural networks; Partial differential equations; Physics; resonant inelastic X-ray scattering; Robotics; spectroscopy; differentiable modelling; resonant inelastic X-ray scattering; machine learning; spectroscopy; artificial intelligence; physics
• ISSN: 2054-5703; 2054-5703
• DOI: 10.1098/rsos.231374

A prevalent class of challenges in modern physics are inverse problems, where physical quantities must be extracted from experimental measurements. End-to-end machine learning approaches to inverse problems typically require constructing sophisticated estimators to achieve the desired accuracy, largely because they need to learn the complex underlying physical model. Here, we discuss an alternative paradigm: by making the physical model auto-differentiable we can construct a neural surrogate to represent the unknown physical quantity sought, while avoiding having to relearn the known physics entirely. We dub this process surrogate training embedded in physics (STEP) and illustrate that it generalizes well and is robust against overfitting and significant noise in the data. We demonstrate how STEP can be applied to perform dynamic kernel deconvolution to analyse resonant inelastic X-ray scattering spectra and show that surprisingly simple estimator architectures suffice to extract the relevant physical information.