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Showing 1 to 20 of 41 for “"Stochastic methods"”.
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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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Stochastic Methods for Setting Effective Aviation NOₓ Policies
… investigated. This work considers effective methods to define this new regulation given a wide range of uncertainties in the tradeoff between NOₓ and CO₂ emissions at high OPRs. First, an estimate for the combined climate and air quality cost of NOₓ from aviation cruise emissions is estimated …
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Stochastic methods for modeling hydrodynamics of dilute gases
… small scale sub-micron gas flows, continuum methods, i.e. Navier Stokes equations, no longer apply. Molecular Dynamics (MD) approaches are then more appropriate. For dilute gases, where particles travel in straight lines for the overwhelming majority of the time, MD methods are inefficient …
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Stochastic Methods for Robust Design of Launch Vehicle Structures
… constructed from isotropic materials. Numerical stochastic methods may provide a cheap and rapid solution to the calculation of knock-down factors for composite shells through their ability to incorporate a wide range of complicated imperfection types intrinsic to composites. Currently, there is …
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Stochastic Methods for Dilemma Zone Protection at Signalized Intersections
… several DZ-associated issues, including the new stochastic safety measure, namely dilemma hazard, that indicates the vehicles' changing unsafe levels when they are approaching intersections, the optimal advance detector configurations for the multi-detector green extension systems, the new …
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Development and Application of Stochastic Methods for Radiation Belt Simulations
… belt Fokker-Planck equation using its equivalent stochastic differential equations, and presents applications of this method to investigating drift shell splitting effects on radiation belt electron phase space density. The theory of the stochastic differential equation method of solving …
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Stochastic methods for improving secondary production decisions under compositional uncertainty
… materials. This work explores the use of stochastic programming techniques which allow explicit consideration of statistical information on composition. The computational complexity of several methods is quantified in order to select a single method for comparison to deterministic models, …
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Stochastic Methods for One-Sided Bipartite Crossing Minimization and its Variants
… minimizing associated edge crossings. Although stochastic methods have been highly successful when applied to bipartite graph drawing, a large, comprehensive study to compare said methods has not been carried out for one-sided crossing minimization on traditional, unweighted graphs. Even more …
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Stochastic methods for large-scale linear problems, variational inequalities, and convex optimization
This thesis considers stochastic methods for large-scale linear systems, variational inequalities, and convex optimization problems. I focus on special structures that lend themselves to sampling, such as when the linear/nonlinear mapping or the objective function is an expected value or is the sum …
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Large-scale models of transient unsaturated flow and contaminant transport using stochastic methods
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1984.
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Application of stochastic methods to transient flow and transport in heterogeneous unsaturated soils
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1990.
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Statistical Kinetics and Nonequilibrium Thermodynamics of Driven Systems: Stochastic Methods and Applications to Single-Molecule Biophysics
… the Poisson indicator, a normalized measure of stochastic variation. A novel pathway analysis framework is extended to nonrenewal processes (i.e., those with correlated inter-event times) and fully reversible processes, accounting for kinetic network complexities, nontrivial event-averaged …
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Nonlinear Dynamics, Stochastic Methods, And Predictive Modelling For Infectious Disease: Application To Public Health And Epidemic Forecasting
… introduces flexible models and statistical methods designed to infer data-generating processes that vary temporally. The primary objective is to develop frameworks for efficient estimation and prediction of both univariate and multivariate time series data. The models considered are general …
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Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand
… as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are …
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ΜΕΛΕΤΗ ΤΩΝ ΒΡΟΧΟΠΤΩΣΕΩΝ ΣΕ ΔΙΑΦΟΡΕΤΙΚΕΣ ΚΛΙΜΑΚΕΣ ΧΩΡΟΥ ΚΑΙ ΧΡΟΝΟΥ: ΕΦΑΡΜΟΓΗ ΣΤΗΝ ΠΕΔΙΑΔΑ ΤΗΣ ΚΕΝΤΡΙΚΗΣ ΜΑΚΕΔΟΝΙΑΣ
… OF THE KNOWN STATISTICAL GEOSTATISTICAL AND STOCHASTIC METHODS IN THE AIM OF THE DETERMINATION OF THE VARIOUS SPACE AND TIME SCALES AND THE CONTROL AND OPTIMIZATION OF THE NETWORK. FOR THE SAME PURPOSE, A NEW METHOD, THE AGGREGATION OF THE DATA IN SPACE, AND THE STANDARDIZATION OF THE …
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Brownian particles interacting with a Newtonian Barrier: Skorohod maps and their use in solving a PDE with free boundary, strong approximation, and hydrodynamic limits.
… system. This technique has the benefit of using stochastic methods to show both existence and uniqueness of the resulting PDE with free boundary condition. In 2001, Frank Knight constructed a stochastic process modeling the one dimensional interaction of two particles, one being Newtonian in the …
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Some practical item selection algorithms in cognitive diagnostic computerized adaptive testing -- smart diagnosis for smart learning
… CD-CAT, compared with the restrictive stochastic methods for fixed-length CD-CAT and SHTVOR for variable-length CD-CAT.
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Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging
… managers rely on traditional statistical and stochastic methods to price assets and develop trading and hedging strategies, deep reinforcement learning has proven to be an effective method to learn optimal policies for pricing and hedging. Machine learning removes the need for various …
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