Polynomial Chaos Methods in Dakota
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Publications
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Jump to search filtersDeployment of Scalable UQ Methods for High-Fidelity Simulation-based Applications within the DOE
Abstract not provided.
Scalable Uncertainty Quantification Methods
Abstract not provided.
Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation
Abstract not provided.
A Posteriori Error Analysis and Adaptive Construction of Surrogate Models
Abstract not provided.
High-dimensional sparse grid interpolation and quadrature using one-dimensional Leja quadrature rules
Abstract not provided.
Rapid Development of an Ice Sheet Climate Application using the Components-Based Approach
Abstract not provided.
Advances in UQ Algorithms for Wind Energy Applications
Abstract not provided.
Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations
Abstract not provided.
Constructing Polynomial Chaos Expansions via Compressed Sensing and Cross Validation
Abstract not provided.
Sparse Polynomial Representations of High Dimensional Models
Abstract not provided.
Adaptive sparse grids for uncertainty quantication Enhancing approximations using a posteriori error estimation
Abstract not provided.
A Discussion of Gaussian Process Models and Polynomial Chaos Methods for Uncertainty Quantification
Abstract not provided.
Locally Adaptive Generalised Sparse Grids
Abstract not provided.
Quantification of Uncertainty in Extreme Scale Computations (QUEST)
Abstract not provided.
Minimal Multi-Element Stochastic Collocation for Uncertainty Quantification of Discontinuous Functions
Journal of Computational Physics
Abstract not provided.
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