12 citations as recorded by crossref.

1. Exact conservation laws for neural network integrators of dynamical systems E. Müller 10.1016/j.jcp.2023.112234
2. Machine learning conservation laws from differential equations Z. Liu et al. 10.1103/PhysRevE.106.045307
3. Learning physical models that can respect conservation laws D. Hansen et al. 10.1016/j.physd.2023.133952
4. A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
5. Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data T. Kircher et al. 10.1016/j.cej.2024.149863
6. Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. 10.5194/gmd-15-6677-2022
7. Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. 10.5194/gmd-16-2037-2023
8. Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. 10.1029/2022MS003235
9. Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
10. Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest H. Sharma et al. 10.1038/s41612-023-00353-y
11. Big Data in Earth system science and progress towards a digital twin X. Li et al. 10.1038/s43017-023-00409-w
12. Interpretable conservation laws as sparse invariants Z. Liu et al. 10.1103/PhysRevE.109.L023301