Stephen Guth
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View article: Reduced order modeling of wave energy systems via sequential Bayesian experimental design and machine learning
Reduced order modeling of wave energy systems via sequential Bayesian experimental design and machine learning Open
Marine energy technologies face significant challenges in ensuring their survivability under extreme ocean conditions. Quantifying extreme load statistics on marine energy structures is essential for reliable structural design; however, th…
View article: Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling
Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling Open
Accurately determining hydrodynamic force statistics is crucial for designing offshore engineering structures, including offshore wind turbine foundations, due to the significant impact of nonlinear wave–structure interactions. However, ob…
View article: Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems Open
Machine learning methods for the construction of data-driven reduced order model models are used in an increasing variety of engineering domains, especially as a supplement to expensive computational fluid dynamics for design problems. An …
View article: Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine foundations using wave episodes and targeted CFD simulations through active sampling
Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine foundations using wave episodes and targeted CFD simulations through active sampling Open
For many design applications in offshore engineering, including offshore wind turbine foundations, engineers need accurate statistics for kinematic and dynamic quantities, such as hydrodynamic forces, whose statistics depend on the stochas…
View article: Discovering and forecasting extreme events via active learning in neural operators
Discovering and forecasting extreme events via active learning in neural operators Open
Extreme events in society and nature, such as pandemic spikes, rogue waves, or structural failures, can have catastrophic consequences. Characterizing extremes is difficult as they occur rarely, arise from seemingly benign conditions, and …
View article: Probabilistic characterization of the effect of transient stochastic\n loads on the fatigue-crack nucleation time
Probabilistic characterization of the effect of transient stochastic\n loads on the fatigue-crack nucleation time Open
The rainflow counting algorithm for material fatigue is both simple to\nimplement and extraordinarily successful for predicting material failure times.\nHowever, it neglects memory effects and time-ordering dependence, and therefore\nruns …
View article: Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems
Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems Open
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 …
View article: Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems
Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems Open
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 …