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We present our ongoing work aimed at accelerating a particle-resolved directnumerical simulation model designed to study aerosol-cloud-turbulenceinteractions. The dynamical model consists of two main components - a set offluid dynamics equations for air velocity, temperature, and humidity, coupledwith a set of equations for particle (i.e., cloud droplet) tracing. Rather thanattempting to replace the original numerical solution method in its entiretywith a machine learning (ML) method, we consider developing a hybrid approach.We exploit the potential of neural operator learning to yield fast and accuratesurrogate models and, in this study, develop such surrogates for the velocityand vorticity fields. We discuss results from numerical experiments designed toassess the performance of ML architectures under consideration as well as theirsuitability for capturing the behavior of relevant dynamical systems.

Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators