Polynomial chaos expansions for travel time and amplitude uncertainty in acoustic time series in ocean waveguides

2006 ◽  
Vol 120 (5) ◽  
pp. 3183-3183
Author(s):  
Kevin D. LePage
Keyword(s):  
Time Series ◽  
Travel Time ◽  
Polynomial Chaos ◽  
Polynomial Chaos Expansions ◽  
Ocean Waveguides

scholarly journals Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Mingjun Deng ◽  
Shiru Qu
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Travel Time ◽  
Nonparametric Regression ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Short Term ◽  
Combination Model ◽  
The Road ◽  
Neighbor Search

There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

Modeling of Stochastic EMC Problems with Anisotropic Polynomial Chaos Expansions

2021 ◽  
Vol 10 (2) ◽  
pp. 70-79
Author(s):  
Theodoros Zygiridis ◽  
Georgios Kommatas ◽  
Aristeides Papadopoulos ◽  
Nikolaos Kantartzis
Keyword(s):  
Polynomial Chaos ◽  
Polynomial Chaos Expansions

scholarly journals mumpce_py: A Python Implementation of the Method of Uncertainty Minimization Using Polynomial Chaos Expansions

2017 ◽  
Vol 122 ◽  
Author(s):  
David A. Sheen
Keyword(s):  
Measurement Uncertainty ◽  
First Principles ◽  
Polynomial Chaos ◽  
Software Tool ◽  
Physical Models ◽  
Physical Parameters ◽  
Experimental Measurements ◽  
Uncertainty Minimization ◽  
Polynomial Chaos Expansions ◽  
Parameter Values

The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.

A Polynomial Chaos Method for Dispersive Electromagnetics

2015 ◽  
Vol 18 (5) ◽  
pp. 1234-1263 ◽  
Author(s):  
Nathan L. Gibson
Keyword(s):  
Polynomial Chaos ◽  
Fdtd Method ◽  
Debye Model ◽  
Dispersive Media ◽  
Dielectric Parameters ◽  
Polarization Model ◽  
Computational Framework ◽  
Spatial Dimensions ◽  
Yee Scheme ◽  
Polynomial Chaos Expansions

AbstractElectromagnetic wave propagation in complex dispersive media is governed by the time dependent Maxwell's equations coupled to equations that describe the evolution of the induced macroscopic polarization. We consider “polydispersive” materials represented by distributions of dielectric parameters in a polarization model. The work focuses on a novel computational framework for such problems involving Polynomial Chaos Expansions as a method to improve the modeling accuracy of the Debye model and allow for easy simulation using the Finite Difference Time Domain (FDTD) method. Stability and dispersion analyzes are performed for the approach utilizing the second order Yee scheme in two spatial dimensions.

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