Volume 64, Issue 2

Search all Computational Mechanics articles

August 2019

Special Issue: Data-Driven Modeling and Simulation—Theory, Methods, and Applications

Issue Editors:

Wing Kam Liu,
George Karniadakis,
Shaoqiang Tang,
Julien Yvonnet

18 articles in this issue

A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
Wing Kam Liu
George Karniadakis
Julien Yvonnet
Editorial 18 June 2019 Pages: 275 - 277
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
Wing Kam Liu
George Karniadakis
Julien Yvonnet
Correction 28 June 2019 Pages: 279 - 279
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
Hengyang Li
Orion L. Kafka
Wing Kam Liu
Original Paper 22 May 2019 Pages: 281 - 305
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
Jinbo Bai
Original Paper 11 October 2018 Pages: 307 - 321
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
Kai Li
Original Paper 18 May 2019 Pages: 323 - 349
Fast calculation of interaction tensors in clustering-based homogenization
Lei Zhang
Shaoqiang Tang
Wing Kam Liu
Original Paper 22 May 2019 Pages: 351 - 364
Derivation of heterogeneous material laws via data-driven principal component expansions
Hang Yang
Xu Guo
Wing Kam Liu
Original Paper 22 May 2019 Pages: 365 - 379
Model-free data-driven methods in mechanics: material data identification and solvers
Laurent Stainier
Adrien Leygue
Michael Ortiz
Original Paper 04 June 2019 Pages: 381 - 393
Solving Bayesian inverse problems from the perspective of deep generative networks
Thomas Y. Hou
Ka Chun Lam
Shumao Zhang
Original Paper 19 June 2019 Pages: 395 - 408
Parametric Gaussian process regression for big data
Maziar Raissi
Hessam Babaee
George Em Karniadakis
Original Paper 09 May 2019 Pages: 409 - 416
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Yibo Yang
Paris Perdikaris
Original Paper 21 May 2019 Pages: 417 - 434
Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
Guorong Chen
Tiange Li
Shaofan Li
Original Paper 17 April 2019 Pages: 435 - 449
Transfer learning of deep material network for seamless structure–property predictions
Zeliang Liu
C. T. Wu
M. Koishi
Original Paper 12 April 2019 Pages: 451 - 465
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
Kun Wang
WaiChing Sun
Qiang Du
Original Paper 25 May 2019 Pages: 467 - 499
Non-parametric material state field extraction from full field measurements
Adrien Leygue
Rian Seghir
Laurent Stainier
Original Paper 23 May 2019 Pages: 501 - 509
Learning slosh dynamics by means of data
B. Moya
D. Gonzalez
E. Cueto
Original Paper 13 April 2019 Pages: 511 - 523
Prediction of aerodynamic flow fields using convolutional neural networks
Saakaar Bhatnagar
Yaser Afshar
Shailendra Kaushik
Original Paper 12 June 2019 Pages: 525 - 545
Integrated Lagrangian and Eulerian 3D microstructure-explicit simulations for predicting macroscopic probabilistic SDT thresholds of energetic materials
Yaochi Wei
Reetesh Ranjan
Min Zhou
Original Paper 05 June 2019 Pages: 547 - 561

Volume 64, Issue 2

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