Grantee Research Project Results
2023 Progress Report: Machine-learned atmospheric chemical mechanisms
EPA Grant Number: R840012Title: Machine-learned atmospheric chemical mechanisms
Investigators: Tessum, Christopher , Riemer, Nicole
Institution: University of Illinois Urbana-Champaign
EPA Project Officer: Chung, Serena
Project Period: August 1, 2020 through July 31, 2023 (Extended to July 31, 2024)
Project Period Covered by this Report: August 1, 2022 through July 31,2023
Project Amount: $399,469
RFA: Chemical Mechanisms to Address New Challenges in Air Quality Modeling (2019) RFA Text | Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Early Career Awards
Objective:
The objective of the work proposed here is to produce a machine-learned condensed chemical mechanism that provides accurate results in air quality modeling simulations under a wide variety of atmospheric conditions. The proposed work builds on preliminary studies where we have created a neural-network-based chemical mechanism that operates with fewer chemical species and orders-of- magnitude less computational cost than the reference mechanism it was trained on, while reproducing diurnal cycles with low error in 90% of randomly initialized simulations. The proposed project will focus on reducing error in the remaining 10% of cases.
Progress Summary:
We have coupled the near-explicit gas-phase Master Chemical Mechanism (MCM) with the state-of-art Particle-resolved Monte Carlo Model for Simulation Aerosol Interactions and Chemistry (PartMC- MOSAIC). The combined multiphase chemical mechanism is a detailed reference for emulation, and we have created datasets for training and testing.
We have created a machine-learned surrogate of simplified chemical mechanism by (1) reducing dimensionality using singular value decomposition to create an interpretably-compressed low- dimensional latent space, and (2) using Sparse Identification of Nonlinear Dynamics (SINDy) to create an differential-equation-based representation of the underlying chemical dynamics in the compressed latent space with reduced stiffness.
In other work that is partially supported by this project we have created probabilistic version of the SINDy-based atmospheric chemistry surrogate model, have created an machine-learned surrogate advection operator, and have begun work on a new framework for geoscientific modeling which is more amenable for integration with machine learning than are current FORTRAN-based methods.
Future Activities:
During the no-cost extension period, we plan to finish Task 2: train and test the surrogate model, and also Task 3: implement the surrogate model in a chemical transport model.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
| Other project views: | All 15 publications | 2 publications in selected types | All 2 journal articles |
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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. |
R840012 (2021) R840012 (2022) R840012 (2023) R840012 (Final) R835873 (2020) R835873 (Final) R840014 (2023) R840014 (Final) |
Exit Exit |
Supplemental Keywords:
Atmospheric chemistry, chemical mechanism, SINDy
Relevant Websites:
Machine-learned atmospheric chemical mechanisms Exit
Earth Science Machine Learning Exit
Progress and Final Reports:
Original AbstractThe 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.