Physics-Constrained Machine Learning for Chemical Engineering

• URL: http://arxiv.org/abs/2508.20649v1
• Date: Thu, 28 Aug 2025 10:52:01 GMT
• Title: Physics-Constrained Machine Learning for Chemical Engineering
• Authors: Angan Mukherjee, Victor M. Zavala,
• Abstract summary: Physics-constrained machine learning (PCML) combines physical models with data-driven approaches to improve reliability, generalizability, and interpretability.<n>This perspective summarizes recent developments and highlights challenges/opportunities in applying PCML to chemical engineering.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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