Physics-Constrained Machine Learning for Chemical Engineering
It identifies key problems for researchers and practitioners in chemical engineering, but is incremental as it summarizes existing developments without presenting new results.
The paper addresses the challenges in applying physics-constrained machine learning to chemical engineering, such as embedding physical knowledge and scaling models, and highlights opportunities in areas like closed-loop experimental design and multi-scale phenomena.
Physics-constrained machine learning (PCML) combines physical models with data-driven approaches to improve reliability, generalizability, and interpretability. Although PCML has shown significant benefits in diverse scientific and engineering domains, technical and intellectual challenges hinder its applicability in complex chemical engineering applications. Key difficulties include determining the amount and type of physical knowledge to embed, designing effective fusion strategies with ML, scaling models to large datasets and simulators, and quantifying predictive uncertainty. This perspective summarizes recent developments and highlights challenges/opportunities in applying PCML to chemical engineering, emphasizing on closed-loop experimental design, real-time dynamics and control, and handling of multi-scale phenomena.