Sébastien Bompas

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All published works

Action	Title	Year	Authors
+	Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties	2024	Binh Duong Nguyen Pavlo Potapenko Aytekin Demirci Kishan Govind Sébastien Bompas Stefan Sandfeld
+ PDF Chat	Accuracy of neural networks for the simulation of chaotic dynamics: Precision of training data vs precision of the algorithm	2020	Sébastien Bompas Bertrand Georgeot D. Guéry-Odelin

Common Coauthors

Coauthor	Papers Together
Aytekin Demirci	1
Stefan Sandfeld	1
Bertrand Georgeot	1
Binh Duong Nguyen	1
Kishan Govind	1
Pavlo Potapenko	1
D. Guéry-Odelin	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	LSTM: A Search Space Odyssey	2016	Klaus Greff Rupesh K. Srivastava Jan Koutník Bas R. Steunebrink Jürgen Schmidhuber	1
+	Chaos in Dynamical Systems	1992	Daniel Zwillinger	1
+	An equation for continuous chaos	1976	Otto E. Rössler	1
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	1
+ PDF Chat	Deterministic Nonperiodic Flow	1963	Edward N. Lorenz	1
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	1
+	WaveNet: A Generative Model for Raw Audio	2016	Aäron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alexander Graves Nal Kalchbrenner Andrew Senior Koray Kavukcuoglu	1
+	Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation	2016	Yonghui Wu Mike Schuster Zhifeng Chen Quoc V. Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey	1
+	Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data	2017	Jaideep Pathak Zhixin Lu Brian R. Hunt Michelle Girvan Edward Ott	1
+	Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations	2018	Maziar Raissi	1
+	An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling	2018	Shaojie Bai J. Zico Kolter Vladlen Koltun	1
+ PDF Chat	Attractor reconstruction by machine learning	2018	Zhixin Lu Brian R. Hunt Edward Ott	1
+ PDF Chat	Data-Driven Microstructure Property Relations	2019	Julian Lißner Felix Fritzen	1
+ PDF Chat	Newton versus the machine: solving the chaotic three-body problem using deep neural networks	2020	Philip G. Breen Christopher N. Foley Tjarda Boekholt Simon Portegies Zwart	1
+ PDF Chat	Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics	2020	Pantelis R. Vlachas Jay Pathak Brian R. Hunt Themistoklis P. Sapsis Michelle Girvan Edward Ott Petros Koumoutsakos	1
+ PDF Chat	Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling	2020	Ruiyang Zhang Yang Liu Hao Sun	1
+	Physics-informed multi-LSTM networks for metamodeling of nonlinear structures	2020	Ruiyang Zhang Yang Liu Hao Sun	1
+ PDF Chat	Comparing different deep learning architectures for classification of chest radiographs	2020	Keno K. Bressem Lisa C. Adams Christoph Erxleben Bernd Hamm Stefan M. Niehues Janis L. Vahldiek	1
+ PDF Chat	Array programming with NumPy	2020	C. R. Harris K. Jarrod Millman Stéfan van der Walt Ralf Gommers Pauli Virtanen David Cournapeau Eric Wieser Julian Taylor Sebastian Berg Nathaniel J. Smith	1
+ PDF Chat	Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network	2020	Ashesh Chattopadhyay Pedram Hassanzadeh Devika Subramanian	1
+ PDF Chat	Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks	2018	Pantelis R. Vlachas Wonmin Byeon Zhong Wan Themistoklis P. Sapsis Petros Koumoutsakos	1
+	Towards advancing the earthquake forecasting by machine learning of satellite data	2021	Pan Xiong Lei Tong Kun Zhang Xuhui Shen R. Battiston Dimitar Ouzounov R. Iuppa Danny Crookes Cheng Long Huiyu Zhou	1
+	Machine learning–accelerated computational fluid dynamics	2021	Dmitrii Kochkov Jamie Smith Ayya Alieva Mengqing Wang Michael P. Brenner Stephan Hoyer	1
+ PDF Chat	Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM	2019	Ashesh Chattopadhyay Pedram Hassanzadeh Devika Subramanian	1
+ PDF Chat	A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity	2023	Mahdad Eghbalian Mehdi Pouragha Richard Wan	1
+ PDF Chat	Challenges and Opportunities for Machine Learning in Multiscale Computational Modeling	2023	Phong Nguyen Joseph B. Choi H. S. Udaykumar Stephen Baek	1
+ PDF Chat	Data‐driven decision‐focused surrogate modeling	2024	Rishabh Gupta Qi Zhang	1