Application of physics-informed neural network in two-phase flow
The influx of research on machine learning and data science from the field of computer science into chemical engineering presents transformative opportunities for chemical engineering paradigms.Among them,physics-informed neural network(PINN)has gained wide attention because it embeds physical equations into neural networks so that the network output satisfies physical laws.This work begins by introducing the algorithm ideas and sampling strategies of PINN.It further discuss various treatment of the PINN loss function,mainly including cases with no observational data,equation reduction,equation discretization,and partial embedding of physical equations.Finally,it provides an overview of recent progress in the application of PINN to areas such as gas-liquid two-phase flow,two-phase flow in porous media,liquid-solid two-phase flow,and heat transfer in two-phase flow.
fluid mechanicsmultiphase flownumerical simulationphysics-informed neural networksampling strategyloss function
万方数据
维普
