ASSESSMENT OF COLLOCATION AND GALERKIN APPROACHES TO PARTIAL DIFFERENTIAL EQUATIONS WITH RANDOM DATA
International Journal of Uncertainty Quantification
Abstract not provided.
Publications
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Jump to search filtersBuilding a PDE with Componenets including shape optimization and embedded UQ
Abstract not provided.
What is Piro
Abstract not provided.
Albany : a trilinos-based PDE application
Abstract not provided.
Stokhos : Trilinos tools for embedded stochastic-Galerkin uncertainty quantification methods
Abstract not provided.
Mathematical and computational tools for predictive simulation of complex coupled systems under uncertainty
Abstract not provided.
Intrusive stochastic Galerkin methods for uncertainty quantification of nonlinear stochastic PDEs
Abstract not provided.
Analysis of intrusive Stochastic Galerkin methods for uncertainty quantification of stochastic PDEs
Abstract not provided.
Sacado: Automatic Differentiation Tools for C++ Codes
Abstract not provided.
A Comparison of Intrusive Stochastic Galerkin Methods for Uncertainty Quantification of Stochastic PDEs
Abstract not provided.
LOCA and Other Trilinos Tools for Analysis of Large-Scale Dynamical Systems
Abstract not provided.
Stokhos: Trilinos Tools for Embedded Stochastic-Galerkin Uncertainty Quantification Methods
Abstract not provided.
High Fidelity Simulation for Semiconductor Electrical Devices in Extreme Environments
Abstract not provided.
Capabilities for Uncertainty in Predictive Science (LDRD Final Report)
Predictive simulation of systems comprised of numerous interconnected, tightly coupled components promises to help solve many problems of scientific and national interest. However predictive simulation of such systems is extremely challenging due to the coupling of a diverse set of physical and biological length and time scales. This report investigates un-certainty quantification methods for such systems that attempt to exploit their structure to gain computational efficiency. The traditional layering of uncertainty quantification around nonlinear solution processes is inverted to allow for heterogeneous uncertainty quantification methods to be applied to each component in a coupled system. Moreover this approach allows stochastic dimension reduction techniques to be applied at each coupling interface. The mathematical feasibility of these ideas is investigated in this report, and mathematical formulations for the resulting stochastically coupled nonlinear systems are developed.
Software needs, challenges and opportunities in polynomial chaos modeling for large-scale applications
Abstract not provided.
A Recent Perspective on Predictive Computational Science: Educational Needs and Opportunities
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Analytic Sensitivity and Uncertainty Computations in Large-Scale Applications via Automatic Differentiation
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Fully-Implicit / Direct-to-steady-state Solution of FE Formulations for Resistive Magneto-Hydrodynamic Systems
Abstract not provided.
Enabling Predictive Simulation Through Embedded Sensitivity Analysis and Uncertainty Quantification
Abstract not provided.
Analysis Tools for Large-scale Simulation with Application to Magnetohydrodynamics Physics
Abstract not provided.
Bifurcation analysis software for large-scale and parallel applications with LOCA (with demonstration)
Abstract not provided.
Benefits of Simulation Codes from Automatic Differentiation of Templated C++
Abstract not provided.
General-Purpose Algorithms for Large-Scale Time-Periodic Flow Problems
Abstract not provided.
Automatic Differentation of C++ Codes With Sacado
Abstract not provided.
Periodic Orbits with 4D
Abstract not provided.
Results 126–150 of 161