Data from: Learning coagulation processes with combinatorially-invariant neural networks

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

doi:10.13012/B2IDB-3904737_V1

Title: Data from: Learning coagulation processes with combinatorially-invariant neural networks

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Abstract

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

Authors:

;

University of Illinois

Publication Date:: Mon Oct 04 00:00:00 UTC 2021

DOE Contract Number:: SC0019192

Research Org.:: University of Illinois at Urbana-Champaign

Sponsoring Org.:: U.S. Department of Energy (DOE)

Subject:: Atmospheric Science; Atmospheric chemistry; Coagulation; Machine learning; Particle-resolved modeling

OSTI Identifier:: 3009816

DOI:: https://doi.org/10.13012/B2IDB-3904737_V1

Citation Formats


                    Wang, Justin, Curtis, Jeffrey H, Riemer, Nicole, and West, Matthew. Data from: Learning coagulation processes with combinatorially-invariant neural networks.  United States: N. p., 2021. 
        Web.  doi:10.13012/B2IDB-3904737_V1.

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                    Wang, Justin, Curtis, Jeffrey H, Riemer, Nicole, & West, Matthew. Data from: Learning coagulation processes with combinatorially-invariant neural networks.  United States.  doi:https://doi.org/10.13012/B2IDB-3904737_V1

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                    Wang, Justin, Curtis, Jeffrey H, Riemer, Nicole, and West, Matthew. 2021.  
"Data from: Learning coagulation processes with combinatorially-invariant neural networks".  United States.  doi:https://doi.org/10.13012/B2IDB-3904737_V1.  https://www.osti.gov/servlets/purl/3009816. Pub date:Mon Oct 04 00:00:00 UTC 2021

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@article{osti_3009816,

  title        = {Data from: Learning coagulation processes with combinatorially-invariant neural networks},

  author       = {Wang, Justin and Curtis, Jeffrey H and Riemer, Nicole and West, Matthew},

  abstractNote = {This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.},

  doi          = {10.13012/B2IDB-3904737_V1},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Oct 04 00:00:00 UTC 2021},

  month        = {Mon Oct 04 00:00:00 UTC 2021}

}

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DOI: https://doi.org/10.13012/B2IDB-3904737_V1

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Learning Coagulation Processes With Combinatorial Neural Networks

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