36 citations as recorded by crossref.

1. Physics-guided deep learning for subsurface characterization: From resource exploration to engineering site investigation—A critical review H. Wen et al. https://doi.org/10.1016/j.jappgeo.2026.106515
2. Predictive Accuracy and Deep Ensemble Estimates of Epistemic Uncertainty in Constrained Neural ODEs for Kinetic Modeling K. Hajireza et al. https://doi.org/10.1021/acs.iecr.6c03344
3. Advancing Identification of Transformation Products and Predicting Their Environmental Fate: The Current State of Machine Learning and Artificial Intelligence in Antibiotic Photolysis S. Alharbi https://doi.org/10.3390/app16010267
4. Case Studies of Dimensionality in Chemical Data A. Blokhuis & R. Pollice https://doi.org/10.1002/ejoc.202400949
5. An interpretable statistical surrogate of activated sludge systems that preserves mass conservation and component non-negativity E. Selerio https://doi.org/10.1016/j.dche.2026.100329
6. Learning physical models that can respect conservation laws D. Hansen et al. https://doi.org/10.1016/j.physd.2023.133952
7. Pe-UMGT: Physics-encoded Sim-to-Real transfer for KPI probabilistic prediction in metal smelting under extreme label scarcity B. Sun et al. https://doi.org/10.1016/j.aei.2026.105135
8. Enabling micro-kinetics based simulation of industrial packed-bed reactors by physics-enhanced neural networks F. Biermann et al. https://doi.org/10.1016/j.cej.2025.163598
9. Explainable Deep Learning for Research on the Synergistic Mechanisms of Multiple Pollutants: A Critical Review C. Liu et al. https://doi.org/10.3390/toxics14040335
10. A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics T. Kumar et al. https://doi.org/10.1080/13647830.2025.2478266
11. Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data T. Kircher et al. https://doi.org/10.1016/j.cej.2024.149863
12. A Nudge to the Truth: Atom Conservation as a Hard Constraint in Models of Atmospheric Composition Using a Species-Weighted Correction P. Sturm & S. Silva https://doi.org/10.1021/acsestair.4c00220
13. Guaranteed Mass-Conserving Deep Learning Model for Stiff Kinetics in Atmospheric Chemistry H. Feng et al. https://doi.org/10.1021/acsearthspacechem.6c00004
14. TransNet: a transport-informed graph neural network for forecasting PM2.5 concentrations across South Korea R. Dimri et al. https://doi.org/10.1038/s44407-026-00052-x
15. Improving air quality assessment using physics-inspired deep graph learning L. Li et al. https://doi.org/10.1038/s41612-023-00475-3
16. Big Data in Earth system science and progress towards a digital twin X. Li et al. https://doi.org/10.1038/s43017-023-00409-w
17. On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data A. Mukherjee & D. Bhattacharyya https://doi.org/10.1016/j.compchemeng.2024.108722
18. Physics-Informed machine learning for turbulent combustion in aerospace propulsion: bridging physical rigour and data intelligence D. Christopher Selvam et al. https://doi.org/10.1080/19942060.2026.2678066
19. Interpretable conservation laws as sparse invariants Z. Liu et al. https://doi.org/10.1103/PhysRevE.109.L023301
20. Robust mechanism discovery with atom conserving chemical reaction neural networks F. Döppel & M. Votsmeier https://doi.org/10.1016/j.proci.2024.105507
21. Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data A. Mukherjee & D. Bhattacharyya https://doi.org/10.1021/acs.iecr.4c01429
22. Exact conservation laws for neural network integrators of dynamical systems E. Müller https://doi.org/10.1016/j.jcp.2023.112234
23. Physics-constrained machine learning for chemical engineering A. Mukherjee & V. Zavala https://doi.org/10.1016/j.coche.2026.101228
24. Machine learning conservation laws from differential equations Z. Liu et al. https://doi.org/10.1103/PhysRevE.106.045307
25. Machine Learning Surrogate Models for Mechanistic Kinetics: Embedding Atom Balance and Positivity T. Kircher & M. Votsmeier https://doi.org/10.1021/acs.jpclett.5c00602
26. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. https://doi.org/10.1186/s13321-024-00825-0
27. Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels T. Kumar et al. https://doi.org/10.3389/fther.2025.1594443
28. Physics informed machine learning model for inverse dynamics in robotic manipulators W. Deng et al. https://doi.org/10.1016/j.asoc.2024.111877
29. Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. https://doi.org/10.5194/gmd-15-6677-2022
30. Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. https://doi.org/10.5194/gmd-16-2037-2023
31. Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. https://doi.org/10.1029/2022MS003235
32. Formation-consumption neural networks for efficient chemical kinetics modeling F. Döppel et al. https://doi.org/10.1016/j.cej.2026.175101
33. Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest H. Sharma et al. https://doi.org/10.1038/s41612-023-00353-y
34. Parallel hybrid ordinary differential equation for modeling biological phosphorus removal modified for enhanced predictive performance and physical interpretability G. Zhao et al. https://doi.org/10.1016/j.jenvman.2025.124932
35. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
36. Learning kinetics from non-ideal reactors by implicitly solved finite volumes and global reaction neural networks T. Kircher & M. Votsmeier https://doi.org/10.1016/j.cej.2025.166749