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Found 3 papers, 1 papers with code
Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem model
In this study, we extend a deep learning (DL) model, which could predict the heave and surge motions of a floating semi-submersible 20 to 50 seconds ahead with good accuracy, to quantify its uncertainty of the predictive time series with the help of the dropout technique.
Predicting heave and surge motions of a semi-submersible with neural networks
With the help of measured waves, the prediction extended 46. 5 s into future with an average accuracy close to 90%.
Shape Deformation and Drag Variation of a Coupled Rigid-flexible System in a Flowing Soap Film
We find the shape of the afterbody is determined by the filament length and flow velocity.
Fluid Dynamics
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