A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction
This study developed a three-module hybrid machine learning framework to reconstruct high-dimensional sparse recipes for textile dyeing. It combines forward quality prediction, sparse inverse reconstruction, and feasibility ranking. The framework uses physics constraints and spectral data to suggest optimal dye concentrations, acting as a decision support tool to minimize costly laboratory trials.