| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
|
Liu, Wing Kam
|
|
|
64 |
2 |
p. 275-277
|
artikel
|
| 2 |
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
|
Wang, Kun
|
|
2019
|
64 |
2 |
p. 467-499
|
artikel
|
| 3 |
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
|
Lu, Xiaoxin
|
|
2018
|
64 |
2 |
p. 307-321
|
artikel
|
| 4 |
Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
|
Chen, Guorong
|
|
2019
|
64 |
2 |
p. 435-449
|
artikel
|
| 5 |
Clustering discretization methods for generation of material performance databases in machine learning and design optimization
|
Li, Hengyang
|
|
2019
|
64 |
2 |
p. 281-305
|
artikel
|
| 6 |
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
|
Yang, Yibo
|
|
2019
|
64 |
2 |
p. 417-434
|
artikel
|
| 7 |
Correction to: A computational mechanics special issue on: data-driven modeling and simulation—theory, methods, and applications
|
Liu, Wing Kam
|
|
2019
|
64 |
2 |
p. 279
|
artikel
|
| 8 |
Derivation of heterogeneous material laws via data-driven principal component expansions
|
Yang, Hang
|
|
2019
|
64 |
2 |
p. 365-379
|
artikel
|
| 9 |
Fast calculation of interaction tensors in clustering-based homogenization
|
Zhang, Lei
|
|
2019
|
64 |
2 |
p. 351-364
|
artikel
|
| 10 |
Integrated Lagrangian and Eulerian 3D microstructure-explicit simulations for predicting macroscopic probabilistic SDT thresholds of energetic materials
|
Wei, Yaochi
|
|
2019
|
64 |
2 |
p. 547-561
|
artikel
|
| 11 |
Learning slosh dynamics by means of data
|
Moya, B.
|
|
2019
|
64 |
2 |
p. 511-523
|
artikel
|
| 12 |
Model-free data-driven methods in mechanics: material data identification and solvers
|
Stainier, Laurent
|
|
2019
|
64 |
2 |
p. 381-393
|
artikel
|
| 13 |
Non-parametric material state field extraction from full field measurements
|
Leygue, Adrien
|
|
2019
|
64 |
2 |
p. 501-509
|
artikel
|
| 14 |
Parametric Gaussian process regression for big data
|
Raissi, Maziar
|
|
2019
|
64 |
2 |
p. 409-416
|
artikel
|
| 15 |
Prediction of aerodynamic flow fields using convolutional neural networks
|
Bhatnagar, Saakaar
|
|
2019
|
64 |
2 |
p. 525-545
|
artikel
|
| 16 |
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
|
Nie, Yinghao
|
|
2019
|
64 |
2 |
p. 323-349
|
artikel
|
| 17 |
Solving Bayesian inverse problems from the perspective of deep generative networks
|
Hou, Thomas Y.
|
|
2019
|
64 |
2 |
p. 395-408
|
artikel
|
| 18 |
Transfer learning of deep material network for seamless structure–property predictions
|
Liu, Zeliang
|
|
2019
|
64 |
2 |
p. 451-465
|
artikel
|
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