将物理规律融入机器学习，提升化工建模的可靠性与可解释性
Physics-Constrained Machine Learning for Chemical Engineering
- 用物理定律约束机器学习，融合先验知识与数据驱动
- 解决复杂化工场景下的泛化性、不确定性量化难题
- 适合做实时控制、多尺度模拟与实验闭环设计的研究者
物理约束机器学习（PCML）通过结合物理模型与数据驱动方法，提升了模型的可靠性、泛化能力与可解释性。尽管已在多个科学与工程领域展现显著优势，但在复杂化工应用中仍面临技术与认知挑战：包括如何确定嵌入的物理知识量与类型、设计有效的机器学习融合策略、实现大规模数据集与仿真器的模型扩展，以及预测不确定性的量化。本文综述了近期进展，重点探讨了在闭环实验设计、实时动态控制及多尺度现象处理中的机遇与挑战。
原文摘要 · Abstract (English)
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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