Grantee Research Project Results
Publications Details for Grant Number R840012
Machine-learned atmospheric chemical mechanisms
RFA:
Chemical Mechanisms to Address New Challenges in Air Quality Modeling (2019)
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| Reference Type | Citation | Progress Report Year | Document Sources |
|---|---|---|---|
| Journal Article | Kelp MM, Jacob DJ, Kutz JN, Marshall JD, Tessum CW. Toward stable, general machine-learned models of the atmospheric chemical system. Journal of Geophysical Research-Atmospheres 2020;125:e2020JD032759. |
R835873 (2020) R835873 (Final) R840012 (2021) R840012 (2022) R840012 (2023) R840012 (Final) R840014 (2023) R840014 (Final) |
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| Journal Article | Yang X, Guo L, Zheng Z, Riemer N, Tessum CW. Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics. Journal of Geophysical Research:Machine Learning and Computation. 2024;1(2):e2024JH000132. |
R840012 (Final) |
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| Paper | Guo L, Yang X, Zheng Z, Riemer N, Tessum CW. Uncertainty quantification in reduced-order gas-phase atmospheric chemistry modeling using ensemble SINDy. arXiv preprint 2024; arXiv:2407.09757. |
R840012 (Final) |
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| Paper | Park M, Zheng Z, Riemer N and Tessum CW. Learned 1-D passive scalar advection to accelerate chemical transport modeling:a case study with GEOS-FP horizontal wind fields. arXiv preprint 2023; arXiv:2309.11035. |
R840012 (2023) R840012 (Final) |
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| Presentation | Guo L,Yang X, Zheng Z, Riemer N, and Tessum CW (2022) Chemical surrogate modeling with uncertainty quantification using a Bayesian Neural ODE. Presented at Atmospheric Chemical Mechanisms Annual Conference, Davis, CA. (Poster). |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Park M, Zheng Z, Riemer N, Tessum CW (2022) Learned 1-D advection solver to accelerate air quality modeling. Presented at The Symbiosis of Deep Learning and Differential Equations Workshop II at the Neural Information Processing Systems Annual Conference, Denver, CO. (Abstract, Paper, and Poster). |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Park M, Zheng Z, Riemer N, and Tessum CW (2023) Toward machine-learned acceleration of passive scalar advection: Model description and verification. Presented at American Meteorological Society 24th Conference on Atmospheric Chemistry, Denver, CO. (Poster). |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Tessum CW (2023) Interpretable machine learning for atmospheric model reduction. Presented at NASA Atmospheric Chemistry and Dynamics Laboratory Seminar Series (Invited Presentation). |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Tessum CW (2021) Kinetic neural networks for atmospheric chemistry surrogate modeling. Presented at American Meteorological Society's 23rd Conference on Atmospheric Chemistry. |
R840012 (2022) R840012 (Final) |
not available |
| Presentation | Tessum CW (2020) Kinetic neural networks for atmospheric chemistry surrogate modeling. Presented at Atmospheric Chemical Mechanisms Conference. |
R840012 (2022) R840012 (Final) |
not available |
| Presentation | Tessum CW (2022) Machine-learned atmospheric chemical mechanisms. Presented at NASA GMAO Seminar Series on Earth System Science. (Invited Presentation). |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Tessum CW (2022) Machine-learned atmospheric chemical mechanisms. Presented at American Meteorological Society 24th Conference on Atmospheric Chemistry (Invited Presentation). |
R840012 (2022) R840012 (Final) |
not available |
| Presentation | Yang X, Riemer N, Zheng Z, and Tessum CW. (2021) Multi-Phase Chemistry Surrogate Modeling with Elemental Mass Conservation Using a Neural ODE. Presented at International Aerosol Modeling Algorithms Conference. |
R840012 (2022) R840012 (Final) |
not available |
| Presentation | Yang X, Guo L, Zheng Z, Riemer N, and Tessum CW (2022) Multi-phase chemistry surrogate modeling with a recurrent neural network. Presented at Atmospheric Chemical Mechanisms Annual Conference, Davis, CA. |
R840012 (2023) R840012 (Final) |
not available |
| Presentation | Tessum CW (2024) Air Quality Model Reduction Using Scientific Machine Learning. Presented at CMAS Conference. |
R840012 (Final) |
not available |
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.