Learning Coagulation Processes With Combinatorial Neural Networks

Wang, Justin L.; Curtis, Jeffrey H.; Riemer, Nicole; West, Matthew

doi:10.1029/2022MS003252

Learning Coagulation Processes With Combinatorial Neural Networks

Journal Article · Thu Dec 08 23:00:00 EST 2022 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1029/2022MS003252· OSTI ID:1902789

^[1]; Curtis, Jeffrey H. ^[2]; ^[3]; ^[4]

Department of Computer Science University of Illinois at Urbana‐Champaign Urbana IL USA
Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign Urbana IL USA, Department of Mechanical Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA
Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign Urbana IL USA
Department of Mechanical Science and Engineering University of Illinois at Urbana Champaign Urbana IL USA

Abstract

Simulating the evolution of a coagulating aerosol or cloud of droplets in a key problem in atmospheric science. We present a proof of concept for modeling coagulation processes using a novel combinatorial neural network (CombNN) architecture. Using two types of data from a high‐detail particle‐resolved aerosol simulation, we show that CombNN models outperform standard neural networks and are competitive in accuracy with traditional state‐of‐the‐art sectional models. These CombNN models could have application in learning coarse‐grained coagulation models for multi‐species aerosols and for learning coagulation models from observed size‐distribution data.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0022130; SC0019192

OSTI ID:: 1902789

Alternate ID(s):: OSTI ID: 1902790
OSTI ID: 1959804

Journal Information:: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 12 Vol. 14; ISSN 1942-2466

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

References (15)

Some problems in particle technology Hulburt, H. M.; Katz, S. Chemical Engineering Science, Vol. 19, Issue 8 https://doi.org/10.1016/0009-2509(64)85047-8	journal	August 1964
Modeling coagulation among particles of different composition and size Jacobson, Mark Z.; Turco, Richard P.; Jensen, Eric J. Atmospheric Environment, Vol. 28, Issue 7 https://doi.org/10.1016/1352-2310(94)90280-1	journal	April 1994
Modeling flocculation processes of fine-grained particles using a size-resolved method: Comparison with published laboratory experiments Xu, Fanghua; Wang, Dong-Ping; Riemer, Nicole Continental Shelf Research, Vol. 28, Issue 19 https://doi.org/10.1016/j.csr.2008.09.001	journal	November 2008
Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model Fast, Jerome D.; Gustafson, William I.; Easter, Richard C. Journal of Geophysical Research, Vol. 111, Issue D21 https://doi.org/10.1029/2005jd006721	journal	January 2006
Simulating the evolution of soot mixing state with a particle‐resolved aerosol model Riemer, N.; West, M.; Zaveri, R. A. Journal of Geophysical Research, Vol. 114, Issue D9 https://doi.org/10.1029/2008JD011073	journal	January 2009
Prognostic Validation of a Neural Network Unified Physics Parameterization Brenowitz, N. D.; Bretherton, C. S. Geophysical Research Letters, Vol. 45, Issue 12 https://doi.org/10.1029/2018GL078510	journal	June 2018
Aerosol Mixing State: Measurements, Modeling, and Impacts Riemer, N.; Ault, A. P.; West, M. Reviews of Geophysics, Vol. 57, Issue 2 https://doi.org/10.1029/2018RG000615	journal	May 2019
Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes Seifert, Axel; Rasp, Stephan Journal of Advances in Modeling Earth Systems, Vol. 12, Issue 12 https://doi.org/10.1029/2020MS002301	journal	November 2020
Deep learning and process understanding for data-driven Earth system science Reichstein, Markus; Camps-Valls, Gustau; Stevens, Bjorn Nature, Vol. 566, Issue 7743 https://doi.org/10.1038/s41586-019-0912-1	journal	February 2019
Deep learning to represent subgrid processes in climate models Rasp, Stephan; Pritchard, Michael S.; Gentine, Pierre Proceedings of the National Academy of Sciences, Vol. 115, Issue 39 https://doi.org/10.1073/pnas.1810286115	journal	September 2018
Simulation of Aerosol Dynamics: A Comparative Review of Mathematical Models Seigneur, Christian; Hudischewskyj, A. Belle; Seinfeld, John H. Aerosol Science and Technology, Vol. 5, Issue 2 https://doi.org/10.1080/02786828608959088	journal	January 1986
Description of Aerosol Dynamics by the Quadrature Method of Moments McGraw, Robert Aerosol Science and Technology, Vol. 27, Issue 2 https://doi.org/10.1080/02786829708965471	journal	January 1997
Long Short-Term Memory Hochreiter, Sepp; Schmidhuber, Jürgen Neural Computation, Vol. 9, Issue 8 https://doi.org/10.1162/neco.1997.9.8.1735	journal	November 1997
Data from: Learning coagulation processes with combinatorially-invariant neural networks Wang, Justin; Curtis, Jeffrey H.; Riemer, Nicole University of Illinois at Urbana-Champaign https://doi.org/10.13012/B2IDB-3904737_V1	dataset	January 2021
Versuch einer mathematischen Theorie der Koagulationskinetik kolloider Lösungen Smoluchowski, M. v. Zeitschrift für Physikalische Chemie, Vol. 92U, Issue 1 https://doi.org/10.1515/zpch-1918-9209	journal	January 1918

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