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Multiscale simulations are demanding in terms of computational resources.
K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural networks 2 (5) (1989) 359–366
1989
Earlier work this paper cites.
D. M. Kreps, Nash equilibrium, in: Game Theory, Springer, 1989, pp. 167–177
1989
Earlier work this paper cites.
M. D. McKay, R. J. Beckman, W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 42 (1) (2000) 55–61
2000
Earlier work this paper cites.
H. J. Böhm, A short introduction to continuum micromechanics, in: Mechanics of microstructured materials, Springer, 2004, pp. 1–40
2004
Earlier work this paper cites.
C. M. Bishop, Pattern recognition and machine learning, springer, 2006
2006
Earlier work this paper cites.
S. Li, G. Wang, Introduction to micromechanics and nanomechanics, World Scientific Publishing Company, 2008
2008
Earlier work this paper cites.
C. Villani, Optimal transport: old and new, Vol. 338, Springer, 2009
2009
Earlier work this paper cites.
D. Xiu, Numerical methods for stochastic computations, in: Numerical Methods for Stochastic Computations, Princeton university press, 2010
2010
Earlier work this paper cites.
F. Willot, D. Jeulin, Elastic and electrical behavior of some randommultiscale highly-contrasted composites, International Journal for Multiscale Computational Engineering 9 (3) (2011)
2011
Earlier work this paper cites.
J. Aboudi, S. M. Arnold, B. A. Bednarcyk, Micromechanics of composite materials: a generalized multiscale analysis approach, Butterworth-Heinemann, 2012
2012
Earlier work this paper cites.
A. Clément, C. Soize, J. Yvonnet, Uncertainty quantification in computational stochastic multiscale analysis of nonlinear elastic materials, Computer Methods in Applied Mechanics and Engineering 254 (2013) 61–82
2013
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, A. Y. Ng, et al., Rectifier nonlinearities improve neural network acoustic models, in: Proc. icml, Vol. 30, Citeseer, 2013, p. 3
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, Advances in neural information processing systems 27 (2014)
2014
Earlier work this paper cites.
J. Vondřejc, J. Zeman, I. Marek, An fft-based galerkin method for homogenization of periodic media, Computers & Mathematics with Applications 68 (3) (2014) 156–173
2014
Earlier work this paper cites.
H. Wang, A. Pietrasanta, D. Jeulin, F. Willot, M. Faessel, L. Sorbier, M. Moreaud, Modelling mesoporous alumina microstructure with 3d random models of platelets, Journal of Microscopy 260 (3) (2015) 287–301
2015
Earlier work this paper cites.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems , software available from tensorflow.org (2015). URL https://www.tensorflow.org/
2015
Earlier work this paper cites.
B. Abdallah, F. Willot, D. Jeulin, Morphological modelling of three-phase microstructures of anode layers using sem images, Journal of microscopy 263 (1) (2016) 51–63
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, Deep learning, Vol. 1, MIT press Cambridge, 2016
2016
Earlier work this paper cites.
T. Dozat, Incorporating nesterov momentum into adam (2016)
2016
Earlier work this paper cites.
T. Cohen, M. Welling, Group equivariant convolutional networks, in: International conference on machine learning, PMLR, 2016, pp. 2990–2999
2016
Earlier work this paper cites.
T. S. Cohen, M. Welling, Steerable cnns, arXiv preprint arXiv:1612.08498 (2016)
2016
Earlier work this paper cites.
M. Vicente, J. Mínguez, D. C. González, The use of computed tomography to explore the microstructure of materials in civil engineering: from rocks to concrete, InTech, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial networks, in: International conference on machine learning, PMLR, 2017, pp. 214–223
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
L. Mosser, O. Dubrule, M. J. Blunt, Reconstruction of three-dimensional porous media using generative adversarial neural networks, Physical Review E 96 (4) (2017) 043309
2017
Earlier work this paper cites.
T. De Geus, J. Vondřejc, J. Zeman, R. Peerlings, M. Geers, Finite strain fft-based non-linear solvers made simple, Computer Methods in Applied Mechanics and Engineering 318 (2017) 412–430
2017
Cited alongside, same era.
J. Zeman, T. W. de Geus, J. Vondřejc, R. H. Peerlings, M. G. Geers, A finite element perspective on nonlinear fft-based micromechanical simulations, International Journal for Numerical Methods in Engineering 111 (10) (2017) 903–926
2017
Cited alongside, same era.
B. Sudret, S. Marelli, J. Wiart, Surrogate models for uncertainty quantification: An overview, in: 2017 11th European conference on antennas and propagation (EUCAP), IEEE, 2017, pp. 793–797
2017
Cited alongside, same era.
2017
Cited alongside, same era.
T. Cohen, M. Weiler, B. Kicanaoglu, M. Welling, Gauge equivariant convolutional networks and the icosahedral cnn, in: International Conference on Machine Learning, PMLR, 2019, pp. 1321–1330
2019
Later among the works it cites.
R. Sutton, The bitter lesson (2019). URL http://www.incompleteideas.net/IncIdeas/BitterLesson.html
2019
Later among the works it cites.
C. Rao, Y. Liu, Three-dimensional convolutional neural network (3d-cnn) for heterogeneous material homogenization, Computational Materials Science 184 (2020) 109850
2020
Later among the works it cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, T. Aila, Analyzing and improving the image quality of stylegan, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8110–8119
2020
Later among the works it cites.
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alphaXiv is searching for related work…
Z. Yang, Y. C. Yabansu, R. Al-Bahrani, W.-k. Liao, A. N. Choudhary, S. R. Kalidindi, A. Agrawal, Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets, Computational Materials Science 151 (2018) 278–287
2018
Cited alongside, same era.
P. Carrara, R. Kruse, D. P. Bentz, M. Lunardelli, T. Leusmann, P. A. Varady, L. De Lorenzis, Improved mesoscale segmentation of concrete from 3d x-ray images using contrast enhancers, Cement and Concrete Composites 93 (2018) 30–42
2018
Cited alongside, same era.
S. Bargmann, B. Klusemann, J. Markmann, J. E. Schnabel, K. Schneider, C. Soyarslan, J. Wilmers, Generation of 3d representative volume elements for heterogeneous materials: A review, Progress in Materials Science 96 (2018) 322–384
2018
Cited alongside, same era.
V. Bortolussi, B. Figliuzzi, F. Willot, M. Faessel, M. Jeandin, Morphological modeling of cold spray coatings, Image Analysis and Stereology 37 (2) (2018) 145–158
2018
Cited alongside, same era.
F. Carminati, A. Gheata, G. Khattak, P. M. Lorenzo, S. Sharan, S. Vallecorsa, Three dimensional generative adversarial networks for fast simulation, in: Journal of Physics: Conference Series, Vol. 1085, IOP Publishing, 2018, p. 032016
2018
Cited alongside, same era.
2018
Cited alongside, same era.
C. C. Aggarwal, et al., Neural networks and deep learning, Springer 10 (2018) 978–3
2018
Cited alongside, same era.
F. Chollet, et al., Deep learning with Python, Vol. 361, Manning New York, 2018
2018
Cited alongside, same era.
2020
Later among the works it cites.
A. Gayon-Lombardo, L. Mosser, N. P. Brandon, S. J. Cooper, Pores for thought: generative adversarial networks for stochastic reconstruction of 3d multi-phase electrode microstructures with periodic boundaries, npj Computational Materials 6 (1) (2020) 1–11
2020
Later among the works it cites.
N. Dey, A. Chen, S. Ghafurian, Group equivariant generative adversarial networks, in: International Conference on Learning Representations, 2020
2020
Later among the works it cites.
H. Wessels, C. Weißenfels, P. Wriggers, The neural particle method–an updated lagrangian physics informed neural network for computational fluid dynamics, Computer Methods in Applied Mechanics and Engineering 368 (2020) 113127
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Kumar, D. M. Kochmann, What machine learning can do for computational solid mechanics, in: Current Trends and Open Problems in Computational Mechanics, 2021
2021
Later among the works it cites.
A. Henkes, I. Caylak, R. Mahnken, A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures, Computer Methods in Applied Mechanics and Engineering 385 (2021) 114070
2021
Later among the works it cites.
2021
Later among the works it cites.
C. Villani, Topics in optimal transportation, Vol. 58, American Mathematical Soc., 2021
2021
Later among the works it cites.
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, T. Aila, Alias-free generative adversarial networks, Advances in Neural Information Processing Systems 34 (2021)
2021
Later among the works it cites.
J. Gui, Z. Sun, Y. Wen, D. Tao, J. Ye, A review on generative adversarial networks: Algorithms, theory, and applications, IEEE Transactions on Knowledge and Data Engineering (2021)
2021
Later among the works it cites.
S. Hong, R. Marinescu, A. V. Dalca, A. K. Bonkhoff, M. Bretzner, N. S. Rost, P. Golland, 3d-stylegan: A style-based generative adversarial network for generative modeling of three-dimensional medical images, in: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, Springer, 2021, pp. 24–34
2021
Later among the works it cites.
J.-W. Lee, N. H. Goo, W. B. Park, M. Pyo, K.-S. Sohn, Virtual microstructure design for steels using generative adversarial networks, Engineering Reports 3 (1) (2021) e12274
2021
Later among the works it cites.
T. Hsu, W. K. Epting, H. Kim, H. W. Abernathy, G. A. Hackett, A. D. Rollett, P. A. Salvador, E. A. Holm, Microstructure generation via generative adversarial network for heterogeneous, topologically complex 3d materials, JOM 73 (1) (2021) 90–102
2021
Later among the works it cites.
M. Schneider, A review of nonlinear fft-based computational homogenization methods, Acta Mechanica (2021) 1–50
2021
Later among the works it cites.
T. Cohen, et al., Equivariant convolutional networks, PhD thesis (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Dey, M. Ren, A. V. Dalca, G. Gerig, Generative adversarial registration for improved conditional deformable templates, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 3929–3941
2021
Later among the works it cites.
H. Wessels, C. Böhm, F. Aldakheel, M. Hüpgen, M. Haist, L. Lohaus, P. Wriggers, Computational homogenization using convolutional neural networks, in: Current Trends and Open Problems in Computational Mechanics, Springer, 2022, pp. 569–579
2022
Closest in time.
A. Kennington, Differential geometry reconstructed: a unified systematic framework, http://www.topology.org/tex/conc/dg.html (2022)
2022
Closest in time.
M. D. Zeiler, D. Krishnan, G. W. Taylor, R. Fergus, Deconvolutional networks, in: 2010 IEEE Computer Society Conference on computer vision and pattern recognition, Springer Cham, 2022
2022
Closest in time.
A. Henkes, H. Wessels, R. Mahnken, Physics informed neural networks for continuum micromechanics, Computer Methods in Applied Mechanics and Engineering 393 (2022) 114790
2022
Closest in time.
doi:10.5281/zenodo.6924533
A. Henkes, H. Wessels, Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Jul 2022) · 2022
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