Learning Coagulation Processes With Combinatorial Neural Networks

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. The climate impacts of aerosols and clouds strongly depend on the number concentration and size distribution of aerosol particles and cloud droplets. A key process in determining these size distributions is the process of coagulation, that is, the process of two particles or droplets colliding and forming a bigger particle. While numerical methods exist to simulate the evolution of particle size distributions, these come with considerable computational cost and furthermore require detailed knowledge of the physical process causing the collisions in the first place, which can contain large uncertainties. Our study demonstrates the development of a machine learning model that addresses both challenges using a new type of neural network.

Automated summary

This study presents a proof of concept for modeling coagulation processes using a novel combinatorial neural network architecture. The results show that the CombNN models outperform standard neural networks and are competitive in accuracy with traditional state-of-the-art sectional models. The findings suggest potential applications of these models in learning coagulation models for multi-species aerosols and from observed size-distribution data.