Ausgabe 2/2019
Special Issue: Data-Driven Modeling and Simulation—Theory, Methods, and Applications
Inhalt (18 Artikel)
A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
Wing Kam Liu, George Karniadakis, Shaoqiang Tang, Julien Yvonnet
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
Wing Kam Liu, George Karniadakis, Shaoqiang Tang, Julien Yvonnet
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
Hengyang Li, Orion L. Kafka, Jiaying Gao, Cheng Yu, Yinghao Nie, Lei Zhang, Mahsa Tajdari, Shan Tang, Xu Guo, Gang Li, Shaoqiang Tang, Gengdong Cheng, Wing Kam Liu
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
Xiaoxin Lu, Dimitris G. Giovanis, Julien Yvonnet, Vissarion Papadopoulos, Fabrice Detrez, Jinbo Bai
Principle of cluster minimum complementary energy of FEM-cluster-based reduced order method: fast updating the interaction matrix and predicting effective nonlinear properties of heterogeneous material
Yinghao Nie, Gengdong Cheng, Xikui Li, Liang Xu, Kai Li
Fast calculation of interaction tensors in clustering-based homogenization
Lei Zhang, Shaoqiang Tang, Cheng Yu, Xi Zhu, Wing Kam Liu
Derivation of heterogeneous material laws via data-driven principal component expansions
Hang Yang, Xu Guo, Shan Tang, Wing Kam Liu
Model-free data-driven methods in mechanics: material data identification and solvers
Laurent Stainier, Adrien Leygue, Michael Ortiz
Solving Bayesian inverse problems from the perspective of deep generative networks
Thomas Y. Hou, Ka Chun Lam, Pengchuan Zhang, Shumao Zhang
Parametric Gaussian process regression for big data
Maziar Raissi, Hessam Babaee, George Em Karniadakis
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Yibo Yang, Paris Perdikaris
Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
Guorong Chen, Tiange Li, Qijun Chen, Shaofei Ren, Chao Wang, Shaofan Li
Transfer learning of deep material network for seamless structure–property predictions
Zeliang Liu, C. T. Wu, M. Koishi
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
Kun Wang, WaiChing Sun, Qiang Du
Non-parametric material state field extraction from full field measurements
Adrien Leygue, Rian Seghir, Julien Réthoré, Michel Coret, Erwan Verron, Laurent Stainier
Learning slosh dynamics by means of data
B. Moya, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto
Prediction of aerodynamic flow fields using convolutional neural networks
Saakaar Bhatnagar, Yaser Afshar, Shaowu Pan, Karthik Duraisamy, Shailendra Kaushik
Integrated Lagrangian and Eulerian 3D microstructure-explicit simulations for predicting macroscopic probabilistic SDT thresholds of energetic materials
Yaochi Wei, Reetesh Ranjan, Ushasi Roy, Ju Hwan Shin, Suresh Menon, Min Zhou