Applied Mathematics Commons^™

Approximation Of The Generalized Singular Value Expansion, Matthew Jacob Roberts

Dissertations, Master's Theses and Master's Reports

Let $X$, $Y$, and $Z$ be real separable Hilbert spaces, let $T:X \to Y$ be a compact operator, and let $L:D(L) \to Z$ be a closed and densely defined linear operator. Then the generalized singular value expansion (GSVE) is an expansion that expresses $T$ and $L$ in terms of a common orthonormal basis. Under certain hypotheses on discretization, the GSVE of an approximate operator pair $(T_j,L_j)$, where $T_j:X_j \to Y_j$ and $L_j:X_j \to Z_j$, converges to the GSVE of $(T,L)$. Error estimates establish a rate of convergence that is consistent with numerical experiments in the case of discretization using piecewise …

High Order Bound-Preserving Discontinuous Galerkin Methods And Their Applications In Petroleum Engineering, Ziyao Xu

Dissertations, Master's Theses and Master's Reports

This report contains researches in the theory of high-order bound-preserving (BP) discontinuous Galerkin (DG) method and their applications in petroleum engineering. It contains both theoretical analysis and numerical experiments. The compressible miscible displacements and wormhole propagation problem, arising in petroleum engineering, is used to describe the evolution of the pressure and concentrations of different components of fluid in porous media. The important physical features of concentration and porosity include their boundedness between 0 and 1, as well as the monotone increasing for porosity in wormhole propagation model. How to keep these properties in the simulation is crucial to the robustness …

Credit Risk Analysis Using Machine Learning And Neural Networks, Dhruv Dhanesh Thanawala

Dissertations, Master's Theses and Master's Reports

A key activity within the banking industry is to extend credit to customers, hence,

credit risk analysis is critical for nancial risk management. There are various methods

used to perform credit risk analysis. In this project, we analyze German and

Australian nancial data from UC Irvine Machine Learning repository, reproducing

results previously published in literature. Further, using the same dataset and various

machine learning algorithms, we attempt to create better models by tuning available

parameters, however, our results are at best comparable to published results.

In this report, we have explained the algorithms and mathematical framework that

goes behind developing …

Multiscale Mathematical Modelling Of Nonlinear Nanowire Resonators For Biological Applications, Rosa Fallahpourghadikolaei

Theses and Dissertations (Comprehensive)

Nanoscale systems fabricated with low-dimensional nanostructures such as carbon nanotubes, nanowires, quantum dots, and more recently graphene sheets, have fascinated researchers from different fields due to their extraordinary and unique physical properties. For example, the remarkable mechanical properties of nanoresonators empower them to have a very high resonant frequency up to the order of giga to terahertz. The ultra-high frequency of these systems attracted the attention of researchers in the area of bio-sensing with the idea to implement them for detection of tiny bio-objects. In this thesis, we originally propose and analyze a mathematical model for nonlinear vibrations of nanowire …

Analysis Of Clmr Trees For European And Asian Option Pricing Under Regime-Switching Jump-Diffusion Models, Yaode Sui

Theses and Dissertations (Comprehensive)

In this paper, we study the convergence rates of the multinomial trees constructed by [Costabile, Leccadito, Massabo and Russo, Journal of Computational and Applied Mathematics, 256 (2014), 152 - 167] for European option pricing under the regime-switching jump-diffusion model, which is named as CLMR tree. We also extend the CLMR tree to the pricing of Asian options under the models. Numerical examples are carried out to confirm the theoretical results and the accuracy of computation.

Reliability Estimation Of Reciprocating Seals Based On Multivariate Dependence Analysis And It's Experimental Validation, Chao Zhang, Rentong Chen, Shaoping Wang, Yujie Qian, Mileta M. Tomovic

Engineering Technology Faculty Publications

Accurate reliability estimation for reciprocating seals is of great significance due to their wide use in numerous engineering applications. This work proposes a reliability estimation method for reciprocating seals based on multivariate dependence analysis of different performance indicators. Degradation behavior corresponding to each performance indicator is first described by the Wiener process. Dependence among different performance indicators is then captured using D-vine copula, and a weight-based copula selection method is utilized to determine the optimal bivariate copula for each dependence relationship. A two-stage Bayesian method is used to estimate the parameters in the proposed model. Finally, a reciprocating seal degradation …

Earthquake Magnitude Prediction Using Support Vector Machine And Convolutional Neural Network, Esther Amfo

Open Access Theses & Dissertations

A deep learning-based method Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for earthquake prediction is proposed. Large-magnitude earthquakes triggered by earthquakes can kill thousands of people and cause millions of dollars worth of economic losses. The accurate prediction of large-magnitude earthquakes is a worldwide problem.

In recent years, deep learning technology that can automatically extract features from mass data has been applied in image recognition, natural language processing, object recognition, etc., with great success. We explore to apply deep learning technology to earthquake prediction, we propose a deep learning method for continuous earthquake prediction using historical seismic events. …