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.
Nagler, Kernel methods for vine copula estimation, Master’s thesis, Technische Universität München (2014)
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Data-driven modeling of multivariate stochastic trajectories -- Application to water waves
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.