Bertrand Rouet-Leduc
Bertrand Rouet-Leduc
Scientist, Geophysics Group (EES 17), Los Alamos National Laboratory
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Machine Learning Predicts Laboratory Earthquakes
B Rouet-Leduc, C Hulbert, N Lubbers, K Barros, C Humphreys, ...
Geophysical Research Letters, 2017
Similarity of fast and slow earthquakes illuminated by machine learning
C Hulbert, B Rouet-Leduc, CX Ren, J Riviere, DC Bolton, C Marone, ...
Nature Geoscience, 2018
Continuous chatter of the Cascadia subduction zone revealed by machine learning
B Rouet-Leduc, C Hulbert, PA Johnson
Nature Geoscience, 2018
Estimating Fault Friction From Seismic Signals in the Laboratory
PAJ Bertrand Rouet-Leduc, Claudia Hulbert, David C. Bolton, Christopher X ...
Geophysical Research Letters, 2018
Nanocathodoluminescence reveals mitigation of the stark shift in InGaN quantum wells by Si doping
JT Griffiths, S Zhang, B Rouet-Leduc, WY Fu, A Bao, D Zhu, DJ Wallis, ...
Nano letters 15 (11), 7639-7643, 2015
Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning
B Rouet-Leduc, K Barros, T Lookman, CJ Humphreys
Scientific Reports 6, 24862, 2016
Spatial adaptive sampling in multiscale simulation
B Rouet-Leduc, K Barros, E Cieren, V Elango, C Junghans, T Lookman, ...
Computer Physics Communications 185 (7), 1857-1864, 2014
Distributed database kriging for adaptive sampling (D2KAS)
D Roehm, RS Pavel, K Barros, B Rouet-Leduc, AL McPherson, ...
Computer Physics Communications 192, 138-147, 2015
Machine learning reveals the state of intermittent frictional dynamics in a sheared granular fault
CX Ren, O Dorostkar, B Rouet‐Leduc, C Hulbert, D Strebel, RA Guyer, ...
Geophysical Research Letters 46 (13), 7395-7403, 2019
Automatized convergence of optoelectronic simulations using active machine learning
B Rouet-Leduc, C Hulbert, K Barros, T Lookman, CJ Humphreys
Applied Physics Letters 111 (4), 043506, 2017
An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia
C Hulbert, B Rouet-Leduc, R Jolivet, PA Johnson
Nature communications 11 (1), 1-8, 2020
Characterizing acoustic signals and searching for precursors during the laboratory seismic cycle using unsupervised machine learning
DC Bolton, P Shokouhi, B Rouet‐Leduc, C Hulbert, J Rivière, C Marone, ...
Seismological Research Letters 90 (3), 1088-1098, 2019
Analysis of defect-related inhomogeneous electroluminescence in InGaN/GaN QW LEDs
CX Ren, B Rouet-Leduc, JT Griffiths, E Bohacek, MJ Wallace, ...
Superlattices and Microstructures 99, 118-124, 2016
Machine learning reveals the seismic signature of eruptive behavior at Piton de la Fournaise volcano
CX Ren, A Peltier, V Ferrazzini, B Rouet‐Leduc, PA Johnson, F Brenguier
Geophysical research letters 47 (3), e2019GL085523, 2020
Probing slow earthquakes with deep learning
B Rouet‐Leduc, C Hulbert, IW McBrearty, PA Johnson
Geophysical research letters 47 (4), e2019GL085870, 2020
The kinetics of heterogeneous nucleation and growth: an approach based on a grain explicit model
B Rouet-Leduc, JB Maillet, C Denoual
Modelling and Simulation in Materials Science and Engineering 22 (3), 035018, 2014
Machine learning and fault rupture: a review
CX Ren, C Hulbert, PA Johnson, B Rouet-Leduc
Advances in Geophysics 61, 57-107, 2020
A silent build-up in seismic energy precedes slow slip failure in the Cascadia Subduction zone
C Hulbert, B Rouet-Leduc, PA Johnson
arXiv preprint arXiv:1909.06787, 2019
Machine learning for materials science
B Rouet-Leduc
University of Cambridge, 2017
A deep Learning approach for detecting transient deformation in InSAR
B Rouet-Leduc, R Jolivet, M Dalaison, PA Johnson, C Hulbert
AGU Fall Meeting 2020, 2020
論文 1–20