Quantifying initial and wind forcing uncertainties in the gulf of Mexico
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Publication:341542
DOI10.1007/s10596-016-9581-4zbMath1349.86034OpenAlexW2416592716MaRDI QIDQ341542
Justin Winokur, Matthieu Le Hénaff, Olivier P. Le Maître, Mohamed Iskandarani, Omar M. Knio, Guotu Li
Publication date: 16 November 2016
Published in: Computational Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10596-016-9581-4
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Cites Work
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- A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions
- A non-adapted sparse approximation of PDEs with stochastic inputs
- Adaptive sparse polynomial chaos expansion based on least angle regression
- Sparse pseudospectral approximation method
- Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems
- Dimension-adaptive tensor-product quadrature
- A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database
- A stochastic projection method for fluid flow. II: Random process
- Least angle regression. (With discussion)
- Coordinate transformation and polynomial chaos for the Bayesian inference of a Gaussian process with parametrized prior covariance function
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Probing the Pareto Frontier for Basis Pursuit Solutions
- Spectral Methods for Uncertainty Quantification
- Uncertainty quantification in chemical systems
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Adaptive Smolyak Pseudospectral Approximations
- Multi‐Resolution‐Analysis Scheme for Uncertainty Quantification in Chemical Systems
- An Explicit Representation of a Stationary Gaussian Process
- A stochastic projection method for fluid flow. I: Basic formulation
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