DATA-DRIVEN DISCOVERY OF DYNAMICS FROM TIME-RESOLVED COHERENT SCATTERING
This software implements a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved coherent X-ray scattering measurements. It employs neural differential equations to parameterize unknown real-space dynamics and incorporates a computational scattering forward model to relate real-space predictions to reciprocal-space observations.
Zhou, Tao↗