7. Bibliography#

1

Thomas Chauve. La recristallisation dynamique dans les matériaux anisotropes : caractérisation et modélisation dans la glace polycristalline. PhD thesis, Université Grenoble Alpes, 2017. Thèse de doctorat dirigée par Montagnat Rentier, Maurine Matériaux, mécanique, génie civil, électrochimie Université Grenoble Alpes (ComUE) 2017. URL: http://www.theses.fr/2017GREAI001/document.

2

T.H. Jacka and M. Maccagnan. Ice crystallographic and strain rate changes with strain in compression and extension. Cold Regions Science and Technology, 8(3):269–286, 1984. URL: https://www.sciencedirect.com/science/article/pii/0165232X84900582, doi:https://doi.org/10.1016/0165-232X(84)90058-2.

3

T. Thorsteinsson, J. Kipfstuhl, and Heinrich Miller. Textures and fabrics in the grip ice core. Journal of Geophysical Research-Oceans, Vol. 102 (C12), pages 26583–26599, 1997.

4

T. Chauve, M. Montagnat, S. Piazolo, B. Journaux, J. Wheeler, F. Barou, D. Mainprice, and A. Tommasi. Non-basal dislocations should be accounted for in simulating ice mass flow. Earth and Planetary Science Letters, 473:247–255, 2017. URL: https://www.sciencedirect.com/science/article/pii/S0012821X17303308, doi:https://doi.org/10.1016/j.epsl.2017.06.020.

5

C. J. L. WILSON, D. S. RUSSELL-HEAD, K. KUNZE, and G. VIOLA. The analysis of quartz c-axis fabrics using a modified optical microscope. Journal of Microscopy, 227(1):30–41, 2007. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2818.2007.01784.x, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2818.2007.01784.x, doi:https://doi.org/10.1111/j.1365-2818.2007.01784.x.

6

H. Moulinec and Pierre Suquet. A numerical method for computing the overall response of nonlinear composites with complex microstructure. Computer Methods in Applied Mechanics and Engineering, April 1998. URL: https://hal.archives-ouvertes.fr/hal-01282728, doi:10.1016/S0045-7825(97)00218-1.

7

P. Suquet, H. Moulinec, O. Castelnau, M. Montagnat, N. Lahellec, F. Grennerat, P. Duval, and R. Brenner. Multi-scale modeling of the mechanical behavior of polycrystalline ice under transient creep. Procedia IUTAM, 3:76–90, 2012. IUTAM Symposium on Linking Scales in Computations: From Microstructure to Macro-scale Properties. URL: https://www.sciencedirect.com/science/article/pii/S2210983812000077, doi:https://doi.org/10.1016/j.piutam.2012.03.006.

8

Nicola Pezzotti. Dimensionality-Reduction Algorithms for Progressive Visual Analytics. PhD thesis, Delft University of Technology, 2019. doi:10.4233/uuid:df6c0760-89ba-4db0-9621-19c512eb1955.

9

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

10

Ziv Ben-Zion, Yoav Zeevi, Nimrod Jackob Keynan, Roee Admon, Tal Kozlovski, Haggai Sharon, Pinchas Halpern, Israel Liberzon, Arieh Y Shalev, Yoav Benjamini, and others. Multi-domain potential biomarkers for post-traumatic stress disorder (ptsd) severity in recent trauma survivors. Translational psychiatry, 10(1):1–11, 2020.

11

Facundo Bre, Juan M. Gimenez, and Víctor D. Fachinotti. Prediction of wind pressure coefficients on building surfaces using artificial neural networks. Energy and Buildings, 158:1429–1441, 2018. URL: https://www.sciencedirect.com/science/article/pii/S0378778817325501, doi:https://doi.org/10.1016/j.enbuild.2017.11.045.

12

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

13

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.

14

Fanny Benoit Grennerat. Hétérogénéités de déformation au cours du fluage transitoire de la glace polycristalline. Mesures par corrélation d'images numériques et modélisation. Theses, Université de Grenoble, December 2011. URL: https://tel.archives-ouvertes.fr/tel-00728174.

15

Harold Hotelling. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 1933.

16

Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 2008.

17

Leo Breiman. Random forests. Machine learning, 45(1):5–32, 2001.

18

Tianqi Chen and Carlos Guestrin. Xgboost: a scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. 2016.