Can I read Bridging Scales in Multiscale Bubble Growth Dynamics with Correlated Fluctuations Using Neural Operator Learning on EtoBox?

Bridging Scales in Multiscale Bubble Growth Dynamics with Correlated Fluctuations Using Neural Operator Learning by Lu, Minglei; Lin, Chensen; Maxey, Martian; Karniadakis, George; Li, Zhen is a scholarly article available to read on EtoBox.

What is Bridging Scales in Multiscale Bubble Growth Dynamics with Correlated Fluctuations Using Neural Operator Learning about?

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 rangin

Author

Lu, Minglei; Lin, Chensen; Maxey, Martian; Karniadakis, George; Li, Zhen

Published

2024

Language

EN