Physics informed neural network model for multi-particle interaction forces  

作　　者：Yuanye Zhou Hongqiang Wang Borun Wu LiGe Wang Xizhong Chen 

机构地区：[1]Shanghai Academy of AI for Science,Shanghai,200232,China [2]College of Software,Beihang University,Beijing,100191,China [3]School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan,030024,China [4]Shenzhen Research Institute of Shandong University,Shenzhen,518057,China [5]Department of Chemical Engineering,School of Chemistry and Chemical Engineering,Shanghai Jiao Tong University,Shanghai,200240,China

出　　处：《Particuology》2025年第1期126-138,共13页颗粒学报(英文版)

基　　金：support from National Natural Science Foundation of China(grant No.22308212);Science and Technology Innovation Committee of Shenzhen Municipality(grant Nos.RCBS 20200714114910354,JCYJ 20220530141016036);the fruitful discussion with Dr.Jerol Soibam from Malardalen University.

摘　　要：The discrete element method(DEM)model calculates interaction forces between each pair of particles.However,it becomes computational expensive especially when the number of particles is large.In this study,a novel artificial neural network(ANN)model is proposed to replace the model of interaction forces between multiple particles in DEM including contact force and electrostatic force.The ANN model combines the residual network(ResNet)with the physics informed neural network(PINN).The physical loss term is derived from the Newton's third law about internal forces in multi-particle system.The performance of the ANN model is evaluated based on the DEM simulation data of 100,200,and 300-particle system in a wall-bounded 2D swirling flow.It is found that the computing time is reduced nearly an order of magnitude(7–10 times)compared with the DEM model.In addition,the accuracy of the ANN model achieves the R^(2)>0.93 with only≤2%particles are not well predicted.

关 键 词：Artificial neural network ResNet PINN MULTIPHASE DEM Particle interaction force 

分 类 号：O415[理学—理论物理]