Data and code for: From Data Chaos to Physically Interpretable Deterministic Mapping

This repository contains the datasets and code associated with the manuscript:"From Data Chaos to Physically Interpretable Deterministic Mapping".Contents:Source code (archived from GitHub release v1.0)Synthetic benchmark datasets (Burgers‘, FitzHugh-Nagumo, Navier-Stokes, and CSTR systems)Example datasets for quick reproductionNote:The real grinding-classification plant data are subject to industrial confidentiality agreements and cannot be publicly released. Access may be granted upon reasonable request and with permission from the industrial partner.