mlsanityData to 14 Oct 2026

Physics-Constrained Machine Learning for Chemical Engineering

arXiv 2508.20649 ↗First posted 28 Aug 2025cs.LG cs.SY eess.SYcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 54 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

19 of 54 references.

References · 19

  1. 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 634
  2. 2019DeepXDE: A deep learning library for solving differential equationscited by 619
  3. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  4. 2021Learning the solution operator of parametric partial differential equations with physics-informed DeepOnetscited by 540
  5. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 527
  6. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 506
  7. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  8. 2019Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Datacited by 262
  9. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 65
  10. 2024HardNet: Hard-Constrained Neural Networks with Universal Approximation Guaranteescited by 31
  11. 2024Physics-Informed Neural Networks with Hard Linear Equality Constraintscited by 30
  12. 2021DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networkscited by 28
7 more
  1. 2025ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projectioncited by 17
  2. 2025Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraintscited by 14
  3. 2021A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processescited by 7
  4. 2025A Simultaneous Approach for Training Neural Differential-Algebraic Systems of Equationscited by 4
  5. 2024Assessment of Uncertainty Quantification in Universal Differential Equationscited by 3
  6. 2025Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networkscited by 3
  7. 2024A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detectioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraintscited by 3
  2. 2026PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projectioncited by 0
  3. 2026Topological Data Analysis for High-Dimensional Dynamic Process Monitoringcited by 0
  4. 2026A tale of perfect fit and phantom optima: how data-driven models can fail in real-time optimizationcited by 0
  5. 2026Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Datacited by 0