Inferring micro-bubble dynamics with physics-informed deep learning

Micro-bubbles and bubbly flows are widely observed and applied to medicine, involves deformation, rupture, and collision of bubbles, phase mixture, etc. We study bubble dynamics by setting up two numerical simulation cases: bubbly flow with a single bubble and multiple bubbles, both confined in the microtube, with parameters corresponding to their medical backgrounds. Both the cases have their medical background applications. Multiphase flow simulation requires high computation accuracy due to possible component losses that may be caused by sparse meshing during the computation. Hence, data-driven methods can be adopted as a useful tool. Based on physics-informed neural networks (PINNs), we propose a novel deep learning framework BubbleNet, which entails three main parts: deep neural networks (DNN) with sub nets for predicting different physics fields; the physics-informed part, with the fluid continuum condition encoded within; the time discretized normalizer (TDN), an algorithm to normalize field data per time step before training. We apply the traditional DNN and our BubbleNet to train the simulation data and predict the physics fields of both the two bubbly flow cases. Results indicate our framework can predict the physics fields more accurately, estimating the prediction absolute errors. The proposed network can potentially be applied to many other engineering fields.