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Learning Invariant Representation of Multiscale hyperelastic Constitutive Law from Sparse Experimental Data

Communications in Computational Physics 2023年第7期34卷 392-417页

作者： Rui He Junzhi Cui Zihao Yang Jieqiong Zhang Xiaofei Guan School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an 710072China LSEC ICMSECAcademy of Mathematics and Systems ScienceCASBeijing 100190China Innovation Center NPU Chongqing Northwestern Polytechnical UniversityChongqing 400000China School of Mathematics Northwest UniversityXi’an 710127China School of Mathematical Sciences Tongji UniversityShanghai 200092China

Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable *** propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic *** on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different *** a dense stress-stretch relation dataset is generated by training a neural network through the FE ***,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this ***,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different *** proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.

关键词： heterogeneous hyperelastic materials data-driven approach multiscale generic constitutive model physics-constrained

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