Uncertainty Quantification for Maxwell\u27s Equations

This dissertation study three different approaches for stochastic electromagnetic fields based on the time domain Maxwell\u27s equations and Drude\u27s model: stochastic Galerkin method, stochastic collocation method, and Monte Carlo class methods. For each method, we study its regularity, stability, and convergence rates. Numerical experiments have been presented to verify our theoretical results. For stochastic collocation method, we also simulate the backward wave propagation in metamaterial phenomenon. It turns out that the stochastic Galerkin method admits the best accuracy property but hugest computational workload as the resultant PDEs system is usually coupled. The Monte Carlo class methods are easy to implement and do parallel computing but the accuracy is relatively low. The stochastic collocation method inherits the advantages of both of these two methods

Field solver technologies for variation-aware interconnect parasitic extraction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 207-213).Advances in integrated circuit manufacturing technologies have enabled high density onchip integration by constantly scaling down the device and interconnect feature size. As a consequence of the ongoing technology scaling (from 45nm to 32nm, 22nm and beyond), geometrical variabilities induced by the uncertainties in the manufacturing processes are becoming more significant. Indeed, the dimensions and shapes of the manufactured devices and interconnect structures may vary by up to 40% from their design intent. The effect of such variabilities on the electrical characteristics of both devices and interconnects must be accurately evaluated and accounted for during the design phase. In the last few years, there have been several attempts to develop variation-aware extraction algorithms, i.e. algorithms that evaluate the effect of geometrical variabilities on the electrical characteristics of devices and interconnects. However, most algorithms remain computationally very expensive. In this thesis the focus is on variation-aware interconnect parasitic extraction. In the first part of the thesis several discretization-based variation-aware solver techniques are developed. The first technique is a stochastic model reduction algorithm (SMOR) The SMOR guarantees that the statistical moments computed from the reduced model are the same as those of the full model. The SMOR works best for problems in which the desired electrical property is contained in an easily defined subspace.(cont.) The second technique is the combined Neumann Hermite expansion (CNHE). The CNHE combines the advantages of both the standard Neumann expansion and the standard stochastic Galerkin method to produce a very efficient extraction algorithm. The CNHE works best in problems for which the desired electrical property (e.g. impedance) is accurately expanded in terms of a low order multivariate Hermite expansion. The third technique is the stochastic dominant singular vectors method (SDSV). The SDSV uses stochastic optimization in order to sequentially determine an optimal reduced subspace, in which the solution can be accurately represented. The SDSV works best for large dimensional problems, since its complexity is almost independent of the size of the parameter space. In the second part of the thesis, several novel discretization-free variation aware extraction techniques for both resistance and capacitance extraction are developed. First we present a variation-aware floating random walk (FRW) to extract the capacitance/resistance in the presence of non-topological (edge-defined) variations. The complexity of such algorithm is almost independent of the number of varying parameters. Then we introduce the Hierarchical FRW to extract the capacitance/resistance of a very large number of topologically different structures, which are all constructed from the same set of building blocks. The complexity of such algorithm is almost independent of the total number of structures. All the proposed techniques are applied to a variety of examples, showing orders of magnitude reduction in the computational time compared to the standard approaches. In addition, we solve very large dimensional examples that are intractable when using standard approaches.by Tarek Ali El-Moselhy.Ph.D

A reduced-basis method for input-output uncertainty propagation in stochastic PDEs

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 123-132).Recently there has been a growing interest in quantifying the effects of random inputs in the solution of partial differential equations that arise in a number of areas, including fluid mechanics, elasticity, and wave theory to describe phenomena such as turbulence, random vibrations, flow through porous media, and wave propagation through random media. Monte-Carlo based sampling 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 functionals of stochastic PDEs. Our approach is based on constructing a reduced-basis model for the quantity of interest that enables to solve the full problem very efficiently. In particular, we apply a reduced-basis technique to the Hybridizable Discontinuous Galerkin (HDG) approximation of the underlying PDE, which allows for a rapid and accurate evaluation of the input-output relationship represented by a functional of the solution of the PDE. The method has been devised for problems where an affine parametrization of the PDE in terms of the uncertain input parameters may be obtained. This particular structure enables us to seek an offline-online computational strategy to economize the output evaluation. Indeed, the offline stage (performed once) is computationally intensive since its computational complexity depends on the dimension of the underlying high-order discontinuous finite element space. The online stage (performed many times) provides rapid output evaluation with a computational cost which is several orders of magnitude smaller than the computational cost of the HDG approximation. In addition, we incorporate two ingredients to the reduced-basis method. First, we employ the greedy algorithm to drive the sampling in the parameter space, by computing inexpensive bounds of the error in the output on the online stage. These error bounds allow us to detect which samples contribute most to the error, thereby enriching the reduced basis with high-quality basis functions. Furthermore, we develop the reduced basis for not only the primal problem, but also the adjoint problem. This allows us to compute an improved reduced basis output that is crucial in reducing the number of basis functions needed to achieve a prescribed error tolerance. Once the reduced bases have been constructed, we employ Monte-Carlo based sampling methods to perform the uncertainty propagation. The main achievement is that the forward evaluations needed for each Monte-Carlo sample are inexpensive, and therefore statistics of the output can be computed very efficiently. This combined technique renders an uncertainty propagation method that requires a small number of full forward model evaluations and thus greatly reduces the computational burden. We apply our approach to study the heat conduction of the thermal fin under uncertainty from the diffusivity coefficient and the wave propagation generated by a Gaussian source under uncertainty from the propagation medium. We shall also compare our approach to stochastic collocation methods and Monte-Carlo methods to assess the reliability of the computations.by Ferran Vidal-Codina.S.M

Error Estimation for Model Order Reduction of Finite Element Parametric Problems

To solve a parametric model in computational electromagnetics, the Finite Element method is often used. To reduce the computational time and the memory requirement, the Finite Element method can be combined with Model Order Reduction Technic like the Proper Orthogonal Decomposition (POD) and the (Discrete) Empirical Interpolation ((D)EI) Methods. These three numerical methods introduce errors of discretisation, reduction and interpolation respectively. The solution of the parametric model will be efficient if the three errors are of the same order and so they need to be evaluated and compared. In this paper, we propose an aposteriori error estimator based on the verification of the constitutive law which estimates the three different errors. An example of application in magnetostatics with 11 parameters is treated where it is shown how the error estimator can be used to control and to improve the accuracy of the solution of the reduced model

Finite element analysis and design optimisation of shaded pole induction motors

SIGLEAvailable from British Library Document Supply Centre-DSC:DX212729 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Uncertainty analyses in computational electromagnetism

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method