Bridging scales in multiscale bubble growth dynamics with correlated
  fluctuations using neural operator learning
Minglei Lu Chensen Lin Martian Maxey George Karniadakis Zhen Li
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. 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.
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 evaluation of the bubble radius under varying external pressure while
containing correct size-dependent stochastic fluctuations in microscale bubble
growth dynamics. The composite neural operator is the first deep learning
surrogate for multiscale bubble growth dynamics that can capture correct
stochastic fluctuations in microscopic fluid phenomena, which sets a new
direction for future research in multiscale fluid dynamics modeling.