Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3417-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3417-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Apr 2022
Development and technical paper |
| 28 Apr 2022
| 28 Apr 2022
Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)
Air Quality Research Center, University of California, Davis, Davis,
California 95616, USA
Anthony S. Wexler
CORRESPONDING AUTHOR
Air Quality Research Center, University of California, Davis, Davis,
California 95616, USA
Department of Mechanical and Aerospace Engineering, University of
California, Davis, Davis, California 95616, USA
Department of Civil and
Environmental Engineering, University of
California, Davis, Davis, California 95616, USA
Department of Land, Air and Water Resources, University of
California, Davis, Davis, California 95616, USA
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Cited
17 citations as recorded by crossref.
- Exact conservation laws for neural network integrators of dynamical systems E. Müller 10.1016/j.jcp.2023.112234
- Machine learning conservation laws from differential equations Z. Liu et al. 10.1103/PhysRevE.106.045307
- Learning physical models that can respect conservation laws D. Hansen et al. 10.1016/j.physd.2023.133952
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
- 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
- Physics informed machine learning model for inverse dynamics in robotic manipulators W. Deng et al. 10.1016/j.asoc.2024.111877
- 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 10.1021/acsestair.4c00220
- Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. 10.5194/gmd-15-6677-2022
- 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
- Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. 10.1029/2022MS003235
- Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
- 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
- Big Data in Earth system science and progress towards a digital twin X. Li et al. 10.1038/s43017-023-00409-w
- On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data A. Mukherjee & D. Bhattacharyya 10.1016/j.compchemeng.2024.108722
- Interpretable conservation laws as sparse invariants Z. Liu et al. 10.1103/PhysRevE.109.L023301
- Robust mechanism discovery with atom conserving chemical reaction neural networks F. Döppel & M. Votsmeier 10.1016/j.proci.2024.105507
- Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data A. Mukherjee & D. Bhattacharyya 10.1021/acs.iecr.4c01429
17 citations as recorded by crossref.
- Exact conservation laws for neural network integrators of dynamical systems E. Müller 10.1016/j.jcp.2023.112234
- Machine learning conservation laws from differential equations Z. Liu et al. 10.1103/PhysRevE.106.045307
- Learning physical models that can respect conservation laws D. Hansen et al. 10.1016/j.physd.2023.133952
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
- 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
- Physics informed machine learning model for inverse dynamics in robotic manipulators W. Deng et al. 10.1016/j.asoc.2024.111877
- 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 10.1021/acsestair.4c00220
- Downscaling atmospheric chemistry simulations with physically consistent deep learning A. Geiss et al. 10.5194/gmd-15-6677-2022
- 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
- Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method P. Sturm et al. 10.1029/2022MS003235
- Improving air quality assessment using physics-inspired deep graph learning L. Li et al. 10.1038/s41612-023-00475-3
- 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
- Big Data in Earth system science and progress towards a digital twin X. Li et al. 10.1038/s43017-023-00409-w
- On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data A. Mukherjee & D. Bhattacharyya 10.1016/j.compchemeng.2024.108722
- Interpretable conservation laws as sparse invariants Z. Liu et al. 10.1103/PhysRevE.109.L023301
- Robust mechanism discovery with atom conserving chemical reaction neural networks F. Döppel & M. Votsmeier 10.1016/j.proci.2024.105507
- Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data A. Mukherjee & D. Bhattacharyya 10.1021/acs.iecr.4c01429
Latest update: 13 Dec 2024
Short summary
Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Large air quality and climate models require vast amounts of computational power. Machine...
