首页|A physics-informed neural network for simulation of finite deformation in hyperelastic-magnetic coupling problems
A physics-informed neural network for simulation of finite deformation in hyperelastic-magnetic coupling problems
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Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite defor-mation problems.In this paper,a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed.Since the solution space consists of two-phase domains,two separate networks are constructed to independently predict the solution for each phase region.In addition,a conscious point allocation strategy is incorporated to en-hance the prediction precision of the PINN in regions characterized by sharp gradients.With the developed framework,the magnetic fields and deformation fields of magne-torheological elastomers(MREs)are solved under the control of hyperelastic-magnetic coupling equations.Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework.Moreover,the ad-vantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated,particularly in handling small data sets,as well as its ability in swiftly and precisely forecasting magnetostrictive motion.
physics-informed neural network(PINN)deep learninghyperelastic-magnetic couplingfinite deformationsmall data set
Lei WANG、Zikun LUO、Mengkai LU、Minghai TANG
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Department of Engineering Mechanics,College of Mechanics and Engineering Sciences,Hohai University,Nanjing 211100,China
School of Mechanical Engineering and Mechanics,Ningbo University,Ningbo 315211,Zhejiang Province,China
National Natural Science Foundation of ChinaNational Natural Science Foundation of China
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