Learning Coagulation Processes With Combinatorial Neural Networks

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numbere2022MS003252
JournalJournal of Advances in Modeling Earth Systems
Issue number12
StatePublished - Dec 2022


  • aerosol
  • coagulation
  • machine learning

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences


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