| Machine Learning Predicts Laboratory Earthquakes B Rouet-Leduc, C Hulbert, N Lubbers, K Barros, C Humphreys, ... Geophysical Research Letters, 2017 | 145 | 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 | 45 | 2018 |
| Continuous chatter of the Cascadia subduction zone revealed by machine learning B Rouet-Leduc, C Hulbert, PA Johnson Nature Geoscience, 2018 | 41* | 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 | 34* | 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 | 29 | 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 | 27 | 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 | 25 | 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 | 22 | 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 | 17 | 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 | 9 | 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 | 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 | 8 | 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 | 8 | 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 | 7 | 2020 |
| Probing slow earthquakes with deep learning B Rouet‐Leduc, C Hulbert, IW McBrearty, PA Johnson Geophysical research letters 47 (4), e2019GL085870, 2020 | 5 | 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 | 4 | 2014 |
| Machine learning and fault rupture: a review CX Ren, C Hulbert, PA Johnson, B Rouet-Leduc Advances in Geophysics 61, 57-107, 2020 | 3 | 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 | 2 | 2019 |
| Machine learning for materials science B Rouet-Leduc University of Cambridge, 2017 | 2 | 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 | 1 | 2020 |
Paul A. JohnsonLos Alamos National Laboratory確認したメール アドレス: lanl.gov
