Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"sparse grid"”.
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Sparse Grid Interpolation
… numerical discretization schemes such as full grids su ers the curse of dimensionality which is still a roadblock for the numerical treatment of high-dimensional problems. The number of basis functions or nodes (grid points) have to be stored and processed depend exponentially on the number of …
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Sparse Grid Interpolation
… numerical discretization schemes such as full grids su ers the curse of dimensionality which is still a roadblock for the numerical treatment of high-dimensional problems. The number of basis functions or nodes (grid points) have to be stored and processed depend exponentially on the number of …
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Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties
… uncertainties modeled as random inputs. Smolyak sparse grid interpolation technique was successfully applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and …
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Efficient Stochastic Collocation Based Variability Analysis Using Model-Order Reduction Techniques
… using the Stochastic Collocation technique. The Sparse Grid algorithm is applied to limit the growth of the computational cost with an increase in the number of stochastic parameters. In addition, the proposed method is based on the Model Order Reduction algorithms coupled with the Numerical …
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Uncertainty analysis of power systems using collocation
… models. The conventional formulation for the sparse grid, a collocation algorithm, is modified to yield improved performance. Theoretical bounds and computational examples are given to support the modification. A dimension-adaptive collocation algorithm is implemented in an unscented Kalman …
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Probabilistic hosting capacity and risk analysis for distribution networks
… networks. The third one is the utilization of sparse grid numerical approximation techniques in handling the uncertainty computations. The last contribution is the new assessment method for quantifying the risk of connecting a large number of correlated distributed generators (DGs) into the …
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Geometric Algorithms and Data Structures for Simulating Diffusion Limited Reactions
… time. The algorithm presented makes use of a sparse grid structure, with one grid per species per radiochemical reactant used to group particles in a way that makes the nearest neighbor search efficient, where particles are stored only once, yet are represented in grids of all appropriate …
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Stochastic modeling in computational electromagnetics
… The proposed techniques are based on the Sparse Grid Collocation method which is a more efficient alternative than the standard Monte Carlo method. The high dimensionality challenge associated with certain stochastic problems, defined in terms of correlated random variables, is alleviated …
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Game-theoretic models for mergers and acquisitions
… in nature) can be adequately approximated by a sparse grid of discrete strategies, providing that these discrete strategies are chosen so as to achieve an even spread across the set of continuous strategies. A sensitivity analysis on the contextual parameters shows that the optimal strategy pair …
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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… generalized Polynomial Chaos Expansion (PCE), Sparse Gird Stochastic Collocation (SGSC) and GP were applied respectively for these three problems to replace the full models during MCMC sampling. We proposed an improved modular Bayesian approach that can avoid extrapolating the model discrepancy …