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Showing 1 to 17 of 17 for “"Stochastic collocation"”.
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Efficient Stochastic Collocation Based Variability Analysis Using Model-Order Reduction Techniques
… is presented in this thesis for time-domain stochastic analysis of large active/passive circuits with multiple stochastic parameters. The new approach reduces the computational cost of variability analysis by using the Stochastic Collocation technique. The Sparse Grid algorithm is applied to …
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The use of stochastic collocation for sampling from expensive distributions with applications in finance
… this, Grzelak et al. (2015) introduced the stochastic collocation Monte Carlo sampler. This sampling method is based on a generalisation of the stochastic collocation method of Mathelin and Hussaini (Mathelin andHussaini, 2003) which was introduced in the context of solving stochastic …
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Stochastic techniques for computational electromagnetics and signal integrity design optimization
… performance. This thesis provides an overview of stochastic numerical techniques in the context of electronic package modeling for signal integrity analysis. Examples are provided to demonstrate the efficiency and accuracy of the Stochastic Collocation method, and its application in computer …
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Techniques for stochastic simulation of complex electromagnetic and circuit systems with uncertainties
… set of tools and methodologies that perform fast stochastic characterization and simulation of uncertainties in electromagnetic and circuit systems. Background information on polynomial chaos and fast stochastic numerical techniques is reviewed, and discussion is offered on comparison of different …
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Stochastic Modeling of Micro-Electromechanical Systems (Mems)
In the final part, a data-driven stochastic collocation approach is presented, which seeks to characterize uncertain input parameters based on available experimental information. This approach models the uncertain parameters as independent random variables, for which the distributions are estimated …
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Uncertainty and sensitivity analysis of computational simulations of industrial jet flows
… simulations of two industrial problems with a stochastic approach. For this purpose, uncertainty quantification and global sensitivity analysis have been carried out in two industrial jet flows with different objectives. First, an impinging swirling air jet for heat transfer purposes is …
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A reduced-basis method for input-output uncertainty propagation in stochastic PDEs
… methods, generalized polynomial chaos and stochastic collocation methods are some of the popular approaches that have been used in the analysis of such problems. This work proposes a non-intrusive reduced-basis method for the rapid and reliable evaluation of the statistics of linear …
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Sensitivity Analysis and Uncertainty Quantification of Plasma Jet Instabilities in the VKI Plasmatron
… and is characterized by a random variable. A stochastic collocation method is implemented to propagate the uncertainty introduced by the electric power to the instability features of the plasma jet. Finally, the sensitivity analysis is carried out again, this time however as a stochastic …
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Stochastic modeling and uncertainty quantification in microelectromechanical systems
… or uncertainties can be represented by stochastic variables which perturb the deterministic behaviour of the device about the nominal value for which it was designed. The UQ process consists of identifying the relevant uncertain parameters, assigning appropriate stochastic models to them …
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Quantification and propagation of nuclear data uncertainties
… (PCE) on the uncertain output quantities, the stochastic collocation method (SCM) is used to compute the PCE coefficients. Compared to the "brute force" Monte Carlo forward propagation method, the PCE-SCM approach is shown to be capable of obtaining the same amount of output quantity …
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Stochastic methods for uncertainty quantification in radiation transport
<p>The use of stochastic spectral expansions, specifically generalized polynomial chaos (gPC) and Karhunen-Loeve (KL) expansions, is investigated for uncertainty quantification in radiation transport. The gPC represents second-order random processes in terms of an expansion of orthogonal …
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Efficient and physically consistent electromagnetic macromodeling of high-speed interconnects exhibiting geometric uncertainties
… by employing the mathematical framework of stochastic collocation and parametric macromodeling to provide for a computationally efficient development of a passive, broadband, stochastic electromagnetic macromodel of the channel over the random space defined by the random variables that …
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Computational Simulation of Chloride-Induced Corrosion Damage in Prestressed Concrete Bridge Girders
… ingress in bridge girders, through the use of a stochastic collocation approach. The focus is on understanding how the inherent uncertainty in the value of input parameters (e.g., material transport parameters, ambient conditions etc.) is propagated, leading to uncertainty in the evolution of …
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Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework
… and rigorously developed metamodels based on stochastic spectral techniques and Gaussian Processes (also known as Kriging) emulators. We demonstrated the developed methodology based on three problems with different levels of sophistication: (1) Point Reactor Kinetics Equation (PRKE) coupled …
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A Variational Approach to Estimating Uncertain Parameters in Elliptic Systems
… we propose a variational approach to solve the stochastic inverse problem of obtaining a statistical description of the diffusion coefficient in an elliptic partial differential equation, based noisy measurements of the model output. We formulate the parameter identification problem as an …
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Uncertainty quantification for integrated circuits and microelectrornechanical systems
… including inaccurate component models, the stochastic nature of some design parameters, external environmental fluctuations (e.g., temperature variation), measurement noise, and so forth. In order to enable robust engineering design and optimal decision making, efficient stochastic solvers …
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Stochastic modeling in computational electromagnetics
… 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 with a …