Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data

[1]  Jamshid Ghaboussi,et al.  Characterizing rate-dependent material behaviors in self-learning simulation , 2006 .

[2]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[3]  Zeliang Liu,et al.  Exploring the 3D architectures of deep material network in data-driven multiscale mechanics , 2019, Journal of the Mechanics and Physics of Solids.

[4]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[5]  Wenbin Yu,et al.  Physics-Informed Artificial Neural Network Approach for Axial Compression Buckling Analysis of Thin-Walled Cylinder , 2020 .

[6]  Wenbin Yu,et al.  Initial failure strength prediction of woven composites using a new yarn failure criterion constructed by deep learning , 2019 .

[7]  Hsuan-Teh Hu,et al.  Failure analysis of fiber-reinforced composite laminates subjected to biaxial loads , 2015 .

[8]  T. Belytschko,et al.  A first course in finite elements , 2007 .

[9]  Stephen W. Tsai,et al.  Nonlinear Elastic Behavior of Unidirectional Composite Laminae , 1973 .

[10]  P. Acar.  Machine Learning Reinforced Crystal Plasticity Modeling Under Experimental Uncertainty , 2020 .

[11]  Bernhard A. Schrefler,et al.  Artificial Neural Networks in numerical modelling of composites , 2009 .

[12]  Kun Wang,et al.  A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning , 2018, Computer Methods in Applied Mechanics and Engineering.

[13]  Wenbin Yu,et al.  A neural network enhanced system for learning nonlinear constitutive relation of fiber reinforced composites , 2020 .

[14]  R. Jones,et al.  Predicting the mechanical response of oligocrystals with deep learning , 2019, Computational Materials Science.

[15]  Kendall E. Atkinson.  An introduction to numerical analysis , 1978 .

[16]  Ronald Davis,et al.  Neural networks and deep learning , 2017 .

[17]  Wenbin Yu,et al.  Two-step homogenization of textile composites using mechanics of structure genome , 2017 .

[18]  J. Chaboche.  Continuum Damage Mechanics: Part I—General Concepts , 1988 .

[19]  Tadanobu Sato,et al.  Neural computations of effective response of random composites , 2000 .

[20]  Wenbin Yu,et al.  Constitutive modeling of damageable brittle and quasi-brittle materials , 2017 .

[21]  B. A. Le,et al.  Computational homogenization of nonlinear elastic materials using neural networks , 2015 .

[22]  Steven M. Arnold,et al.  Multiscale Analysis of Composites Using Surrogate Modeling and Information Optimal Designs , 2020 .

[23]  Wenbin Yu,et al.  A Unified Theory for Constitutive Modeling of Composites , 2016 .

[24]  Jin-Song Pei,et al.  Constructing Multilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering Mechanics Applications , 2008 .

[25]  Wei Chen,et al.  A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality , 2017 .

[26]  Wenbin Yu,et al.  Multiscale analysis of woven composite structures in MSC.Nastran , 2019, Adv. Eng. Softw..

[27]  Wenbin Yu,et al.  A composite beam theory for modeling nonlinear shear behavior , 2018 .

[28]  Liang Zhang,et al.  VUMAT of a Generalized Standard Continuum Damage Model , 2018 .