Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of der...

Disgrifiad llawn

Manylion Llyfryddiaeth
Prif Awduron:	Guth, Stephen Carrol, Sapsis, Themistoklis Panagiotis
Awduron Eraill:	Massachusetts Institute of Technology. Department of Mechanical Engineering
Fformat:	Erthygl
Iaith:	English
Cyhoeddwyd:	MDPI AG 2020
Mynediad Ar-lein:	https://hdl.handle.net/1721.1/125399

Eitemau Tebyg

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Cyhoeddwyd: (2021)

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Statistics of Extreme Events in Fluid Flows and Waves
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Cyhoeddwyd: (2022)

Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time
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Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systems
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Machine-learning mesoscale and submesoscale surface dynamics from lagrangian ocean drifter trajectories
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Surface waves enhance particle dispersion
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Dynamically orthogonal field equations for stochastic fluid flows and particle dynamics
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Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics
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Output-weighted sampling for multi-armed bandits with extreme payoffs
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Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements
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Bayesian optimization with output-weighted optimal sampling
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