A data-driven stochastic model for water wave kinematics is built by combining functional PCA feature reduction with vine copulas for the bulk distribution and Heffernan-Tawn conditional modeling for the tails, enabling synthetic trajectory generation under a breaking constraint.
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Introduces group sparsity constraint and soft energy lower bound in compressed sensing to reconstruct directional wave spectra from sparse multi-channel buoy data.
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Recovery of directional wave spectrum from sparse data with compressed sensing
Introduces group sparsity constraint and soft energy lower bound in compressed sensing to reconstruct directional wave spectra from sparse multi-channel buoy data.