Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning

no code implementations • 20 Mar 2024 • Minglei Lu, Chensen Lin, Martian Maxey, George Karniadakis, Zhen Li

In order to bridge the gap between microscale stochastic fluid models and continuum-based fluid models for bubble dynamics, we develop a composite neural operator model to unify the analysis of nonlinear bubble dynamics across microscale and macroscale regimes by integrating a many-body dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, which consists of a deep operator network for learning the mean behavior of bubble growth subject to pressure variations and a long short-term memory network for learning the statistical features of correlated fluctuations in microscale bubble dynamics.

Operator learning