Neural operator learning for multiscale bubble growth dynamics with correlated fluctuations

The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scale-specific models. To bridge the gap between microscale stochastic models and continuum-based 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 multiphase dissipative particle dynamics (mDPD) model with a continuum-based Rayleigh-Plesset (RP) model through a novel neural network architecture, consisting of an operator network for learning mean-field behavior of bubble growth subject to pressure variations and a long short-term memory network for learning statistical features of correlated fluctuations in microscale dynamics. Training and testing data are generated by conducting mDPD and RP simulations for nonlinear bubble dynamics with initial bubble radii ranging from 0.1 to 1.5 micrometers. Results show that the trained composite neural operator model can accurately predict bubble dynamics across scales, with a 99% accuracy for the time evolution of the bubble radius under varying external pressure while containing correct size-dependent stochastic fluctuations in microscale bubble dynamics.