A perspective on physics-constrained machine learning for chemical engineering that summarizes approaches, applications, and open challenges without introducing a new method.
A novel temperature prediction method without using energy equation based on physics-informed neural network (pinn): A case study on plate- circular/square pin-fin heat sinks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Physics-Constrained Machine Learning for Chemical Engineering
A perspective on physics-constrained machine learning for chemical engineering that summarizes approaches, applications, and open challenges without introducing a new method.