B
Bertrand Rouet-Leduc
Researcher at Los Alamos National Laboratory
Publications - 42
Citations - 1032
Bertrand Rouet-Leduc is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Slip (materials science) & Fault (geology). The author has an hindex of 12, co-authored 38 publications receiving 603 citations. Previous affiliations of Bertrand Rouet-Leduc include École Normale Supérieure & University of Cambridge.
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Machine Learning Predicts Laboratory Earthquakes
Bertrand Rouet-Leduc,Bertrand Rouet-Leduc,Claudia Hulbert,Nicholas Lubbers,Nicholas Lubbers,Kipton Barros,Colin J. Humphreys,Paul A. Johnson +7 more
TL;DR: In this article, the authors apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes, and infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace.
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Continuous chatter of the Cascadia subduction zone revealed by machine learning
TL;DR: In this article, the authors show that the Cascadia subduction zone is apparently continuously broadcasting a low-amplitude, tremor-like signal that precisely informs of the fault displacement rate throughout the slow slip cycle.
Journal ArticleDOI
Machine Learning Predicts Laboratory Earthquakes
Bertrand Rouet-Leduc,Bertrand Rouet-Leduc,Claudia Hulbert,Nicholas Lubbers,Nicholas Lubbers,Kipton Barros,Colin J. Humphreys,Paul A. Johnson +7 more
TL;DR: In this paper, the authors used machine learning to predict the time remaining before a laboratory fault fails with great accuracy, based on the instantaneous physical characteristics of the acoustical signal, and do not make use of its history.
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Similarity of fast and slow earthquakes illuminated by machine learning
Claudia Hulbert,Bertrand Rouet-Leduc,Paul A. Johnson,Christopher X. Ren,Jacques Rivière,David C. Bolton,Chris Marone +6 more
TL;DR: In this article, the authors show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure.
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Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning
TL;DR: This work simulates GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region and rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.