Applied Mathematics Commons^™

Periodicity And Invertibility Of Lattice Gas Cellular Automata, Jiawen Wang

Mathematical Sciences Technical Reports (MSTR)

A cellular automaton is a type of mathematical system that models the behavior of a set of cells with discrete values in progressing time steps. The often complicated behaviors of cellular automata are studied in computer science, mathematics, biology, and other science related fields. Lattice gas cellular automata are used to simulate the movements of particles. This thesis aims to discuss the properties of lattice gas models, including periodicity and invertibility, and to examine their accuracy in reflecting the physics of particles in real life. Analysis of elementary cellular automata is presented to introduce the concept of cellular automata and …

Do Metabolic Networks Follow A Power Law? A Psamm Analysis, Ryan Geib, Lubos Thoma, Ying Zhang

Senior Honors Projects

Inspired by the landmark paper “Emergence of Scaling in Random Networks” by Barabási and Albert, the field of network science has focused heavily on the power law distribution in recent years. This distribution has been used to model everything from the popularity of sites on the World Wide Web to the number of citations received on a scientific paper. The feature of this distribution is highlighted by the fact that many nodes (websites or papers) have few connections (internet links or citations) while few “hubs” are connected to many nodes. These properties lead to two very important observed effects: the …

Scalable Time-Stepping For Navier-Stokes Through High-Frequency Analysis Of Block Arnoldi Iteration, Brianna Bingham

Dissertations

Existing time-stepping methods for PDEs such as Navier-Stokes equations are not as efficient or scalable as they need to be for high-resolution simulation due to stiffness. The failure of existing time-stepping methods to adapt to changes in technology presents a dilemma that is becoming even more problematic over time. By rethinking approaches to time-stepping, dramatic gains in efficiency of simulation methods can be achieved. Krylov subspace spectral (KSS) methods have proven to be effective for solving time-dependent, variable-coefficient PDEs. The objective of this research is to continue the development of KSS methods to provide numerical solution methods that are far …

Enhancement Of Krylov Subspace Spectral Methods Through The Use Of The Residual, Haley Dozier

Dissertations

Depending on the type of equation, finding the solution of a time-dependent partial differential equation can be quite challenging. Although modern time-stepping methods for solving these equations have become more accurate for a small number of grid points, in a lot of cases the scalability of those methods leaves much to be desired. That is, unless the timestep is chosen to be sufficiently small, the computed solutions might exhibit unreasonable behavior with large input sizes. Therefore, to improve accuracy as the number of grid points increases, the time-steps must be chosen to be even smaller to reach a reasonable solution. …

Why Some Non-Classical Logics Are More Studied?, Olga Kosheleva, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

It is well known that the traditional 2-valued logic is only an approximation to how we actually reason. To provide a more adequate description of how we actually reason, researchers proposed and studied many generalizations and modifications of the traditional logic, generalizations and modifications in which some rules of the traditional logic are no longer valid. Interestingly, for some of such rules (e.g., for law of excluded middle), we have a century of research in logics that violate this rule, while for others (e.g., commutativity of ``and''), practically no research has been done. In this paper, we show that fuzzy …

Mathematical Model Investigating The Effects Of Neurostimulation Therapies On Neural Functioning: Comparing The Effects Of Neuromodulation Techniques On Ion Channel Gating And Ionic Flux Using Finite Element Analysis, Kaia Lindberg

Mathematics Theses

Neurostimulation therapies demonstrate success as a medical intervention for individuals with neurodegenerative diseases, such as Parkinson’s and Alzheimer’s disease. Despite promising results from these treatments, the influence of an electric current on ion concentrations and subsequent transmembrane voltage is unclear. This project focuses on developing a unique cellular-level mathematical model of neurostimulation to better understand its e↵ects on neuronal electrodynamics. The mathematical model presented here integrates the Poisson-Nernst-Planck system of PDEs and Hodgkin-Huxley based ODEs to model the e↵ects of this neurotherapy on transmembrane voltage, ion channel gating, and ionic mobility. This system is decoupled using the Gauss-Seidel method and …

Understanding Water Consumption And Energy Trends In New York City, Wen Yong Huang, Johann Thiel

Publications and Research

In this study, we will be using the NYC Open Data website to examine publicly available data sets on water and energy consumption in New York City. In particular, we will use various scientific programming and machine learning modules in Python to analyze and visualize trends in water and energy usage within the five boroughs.

Stability Analysis Of A More General Class Of Systems With Delay-Dependent Coefficients, Chi Jin, Keqin Gu, Islam Boussaada, Silviu-Iulian Niculescu

SIUE Faculty Research, Scholarship, and Creative Activity

This paper presents a systematic method to analyse the stability of systems with single delay in which the coefficient polynomials of the characteristic equation depend on the delay. Such systems often arise in, for example, life science and engineering systems. A method to analyze such systems was presented by Beretta and Kuang in a 2002 paper, but with some very restrictive assumptions. This work extends their results to the general case with the exception of some degenerate cases. It is found that a much richer behavior is possible when the restrictive assumptions are removed. The interval of interest for the …

Comparative Error Analysis Of The Black-Scholes Equation, Chuan Chen

Honors Theses

Finance is a rapidly growing area in our banking world today. With this ever-increasing development come more complex derivative products than simple buy-and-sell trades. Financial derivatives such as futures and options have been developed stemming from the traditional stock, bond, currency, and commodity markets. Consequently, the need for more sophisticated mathematical modeling is also rising. The Black-Scholes equation is a partial differential equation that determines the price of a financial option under the Black-Scholes model. The idea behind the equation is that there is a perfect and risk-free way for one to hedge the options by buying and selling the …

Ergodicity For The 3d Stochastic Navier-Stokes Equations Perturbed By Lévy Noise, Manil T. Mohan, K. Sakthivel, Sivaguru S. Sritharan

Faculty Publications

In this work we construct a Markov family of martingale solutions for 3D stochastic Navier–Stokes equations (SNSE) perturbed by Lévy noise with periodic boundary conditions. Using the Kolmogorov equations of integrodifferential type associated with the SNSE perturbed by Lévy noise, we construct a transition semigroup and establish the existence of a unique invariant measure. We also show that it is ergodic and strongly mixing.
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Combinatorial Optimization: Introductory Problems And Methods, Erin Brownell

Honors Scholar Theses

This paper will cover some topics of combinatorial optimization, the study of finding the best possible arrangement of a set of discrete objects. These topics include the shortest path problem and network flows, which can be extended to solve more complex problems. We will also briefly cover some basics of graph theory and solving linear programming problems to give context to the reader.

Dynamic Attribute-Level Best Worst Discrete Choice Experiments, Amanda Working, Mohammed Alqawba, Norou Diawara

Mathematics & Statistics Faculty Publications

Dynamic modelling of decision maker choice behavior of best and worst in discrete choice experiments (DCEs) has numerous applications. Such models are proposed under utility function of decision maker and are used in many areas including social sciences, health economics, transportation research, and health systems research. After reviewing references on the study of such experiments, we present example in DCE with emphasis on time dependent best-worst choice and discrimination between choice attributes. Numerical examples of the dynamic DCEs are simulated, and the associated expected utilities over time of the choice models are derived using Markov decision processes. The estimates are …

Two-Point Boundary Value Problems For Higher Order Nonlinear Hyperbolic Equations, Audison Beaubrun

Theses and Dissertations

Two–point boundary value problems in a multidimensional box for higher order nonlinear hyperbolic equations are considered. The concepts of a strongly isolated solution, and locally and globally strong well–posedness of a nonlinear boundary value problem are introduced. For general two–point boundary value problems and periodic problems there are established: (i) Necessary and sufficient conditions of locally and globally strong well–posedness; (ii) Unimprovable Sufficient conditions of solvability. For the Dirichlet and Periodic type problems for equations of even order there are established: (i) Effective sufficient conditions of solvability and locally strong well–posedness; (ii) Unimprovable sufficient conditions of solvability for the case, …

Qualitative Analysis Of The Nonlinear Double Degenerate Parabolic Equation Of Turbulent Filtration With Absorption, Adam Prinkey

Theses and Dissertations

The goal of the dissertation is to pursue qualitative analysis of the mathematical model of turbulent polytropic filtration of a gas in a porous media with reaction or absorption described by the second order nonlinear double degenerate parabolic equation ∂u ∂t − ∂ ∂x F [ ∂u m ∂x ] + Q(u) = 0, (1) where F(y) = |y| p−1 y, Q(u) = buβ , m, p, β > 0, b ∈ R. In the absence of the reaction term there is a finite speed of propagation with an expanding interface in the case of slow diffusion (mp > 1), and infinite …

Numerical Study In The Conservative Arbitrary Lagrangian-Eulerian (Ale) Method For An Unsteady Stokes/Parabolic Interface Problem With Jump Coefficients And A Moving Interface, Michael Joseph Ramirez

UNLV Theses, Dissertations, Professional Papers, and Capstones

Towards numerical analyses for fluid-structure interaction (FSI) problems in the future, in this thesis the arbitrary Lagrangian-Eulerian (ALE) finite element method within a conservative form is developed and analyzed for a linearized FSI problem - an unsteady Stokes/parabolic interface problem with jump coefficients and moving interface, and the corresponding mixed finite element approximation is developed and analyzed for both semi- and fully discrete schemes based upon the so-called conservative formulation. In terms of a novel H1-projection technique, their stability and optimal convergence properties are obtained for approximating the real solution equipped with lower regularity.

Numerical Analysis And Fluid Flow Modeling Of Incompressible Navier-Stokes Equations, Tahj Hill

UNLV Theses, Dissertations, Professional Papers, and Capstones

The Navier-Stokes equations (NSE) are an essential set of partial differential equations for governing the motion of fluids. In this paper, we will study the NSE for an incompressible flow, one which density ρ = ρ0 is constant.

First, we will present the derivation of the NSE and discuss solutions and boundary conditions for the equations. We will then discuss the Reynolds number, a dimensionless number that is important in the observations of fluid flow patterns. We will study the NSE at various Reynolds numbers, and use the Reynolds number to write the NSE in a nondimensional form.

We will …

Generalized Random Measures On Topological Spaces, Ali Hussein Mahmood Al-Obaidi

Theses and Dissertations

Our work deals with classes of random measures on -compact Hausdorff spaces perturbed by stochastic processes. We render a rigorous construction of the stochastic integral of functions of two variables and show that such an integral is a random measure. We establish a new Campbell-type formula that, along with a rigorous construction of modulation, leads to the intensity of a modulated random measure. We further introduce and study a marked Poisson random measure on a - compact Hausdorff space. The underlying parameters of this measure are changing in accordance with the evolution of a stochastic process. This generalized random measure …

On The Complexity Of Computing Galois Groups Of Differential Equations, Mengxiao Sun

Dissertations, Theses, and Capstone Projects

The differential Galois group is an analogue for a linear differential equation of the classical Galois group for a polynomial equation. An important application of the differential Galois group is that a linear differential equation can be solved by integrals, exponentials and algebraic functions if and only if the connected component of its differential Galois group is solvable. Computing the differential Galois groups would help us determine the existence of the solutions expressed in terms of elementary functions (integrals, exponentials and algebraic functions) and understand the algebraic relations among the solutions.

Hrushovski first proposed an algorithm for computing the differential …

Mathematical Models: The Lanchester Equations And The Zombie Apocalypse, Hailey Bauer

Undergraduate Theses and Capstone Projects

This research study used mathematical models to analyze and depicted specific battle situations and the outcomes of the zombie apocalypse. The original models that predicted warfare were the Lanchester models, while the zombie apocalypse models were fictional expansions upon mathematical models used to examine infectious diseases. In this paper, I analyzed and compared different mathematical models by examining each model’s set of assumptions and the impact of the change in variables on the population classes. The purpose of this study was to understand the basics of the discrete dynamical systems and to determine the similarities between imaginary and realistic models. …

Realization Of Tensor Product And Of Tensor Factorization Of Rational Functions, Daniel Alpay, Izchak Lewkowicz

Mathematics, Physics, and Computer Science Faculty Articles and Research

We study the state space realization of a tensor product of a pair of rational functions. At the expense of “inflating” the dimensions, we recover the classical expressions for realization of a regular product of rational functions. Under an additional assumption that the limit at infinity of a given rational function exists and is equal to identity, we introduce an explicit formula for a tensor factorization of this function.

Mathematical Modeling: Lanchester Equations And The Zombie Apocalypse, Hailey Bauer

Student Scholar Showcase

Mathematical models are systems using mathematical concepts to explain various problems. Mathematical models examine realistic issues such as the different outcomes of wars and fictional problems such as the upcoming zombie apocalypse. The original predictor models of warfare were the Lanchester models, while the zombie apocalypse is a fictional expansion upon mathematical models for infectious diseases. In this paper, we examined and compared the basic versions of these dynamic models by analyzing each model’s set of variables, assumptions, and objectives. The basic versions of the Lanchester equations are the area aimed model and the aimed fire model. While the zombie …

A More Powerful Unconditional Exact Test Of Homogeneity For 2 × C Contingency Table Analysis, Louis Ehwerhemuepha, Heng Sok, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

The classical unconditional exact p-value test can be used to compare two multinomial distributions with small samples. This general hypothesis requires parameter estimation under the null which makes the test severely conservative. Similar property has been observed for Fisher's exact test with Barnard and Boschloo providing distinct adjustments that produce more powerful testing approaches. In this study, we develop a novel adjustment for the conservativeness of the unconditional multinomial exact p-value test that produces nominal type I error rate and increased power in comparison to all alternative approaches. We used a large simulation study to empirically estimate the …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

The Mathematical Modeling Of Ballet, Kendall Gibson

Mathematics Senior Capstone Papers

This project aims to analyze the connections between ballet and mathematics. Specifically, this project focuses on analyzing the three-dimensional surfaces created as a dancer performs ballet choreography. The primary goal is to use a Vicon motion capture system in conjunction with MATLAB to model the three-dimensional lines and surfaces created by a dancer’s legs as she performs specific ballet movements. The movements used for this experiment were a pique turn and a rond de jambe. The data was collected using sensors to create objects in Vicon to record the position of the ankle, knee, and hip of the working leg …

Reducing Memory Access Latencies Using Data Compression In Sparse, Iterative Linear Solvers, Neil Lindquist

All College Thesis Program, 2016-2019

Solving large, sparse systems of linear equations plays a significant role in certain scientific computations, such as approximating the solutions of partial differential equations. However, solvers for these types of problems usually spend most of their time fetching data from main memory. In an effort to improve the performance of these solvers, this work explores using data compression to reduce the amount of data that needs to be fetched from main memory. Some compression methods were found that improve the performance of the solver and problem found in the HPCG benchmark, with an increase in floating point operations per second …

Predicting Win Rates In Competitive Overwatchtm, Andrea Sibley

Mathematics Senior Capstone Papers

OverwatchTM is a video game published by Blizzard Entertainment R where two teams comprised of six people each compete against one another to accomplish a specific goal. The goal of each game is dependent on which map is being played. The maps are divided into four categories: Assault, Escort, Control, and Hybrid. A data set comprised of 3000 games of competitive OverwatchTM is used to determine how likely a team is to win their match. The factors used to determine the likelihood of winning are the map type and the skill ranking for each team. The data set is pre-processed …

Parameter Estimation And Optimal Design Techniques To Analyze A Mathematical Model In Wound Healing, Nigar Karimli

Masters Theses & Specialist Projects

For this project, we use a modified version of a previously developed mathematical model, which describes the relationships among matrix metalloproteinases (MMPs), their tissue inhibitors (TIMPs), and extracellular matrix (ECM). Our ultimate goal is to quantify and understand differences in parameter estimates between patients in order to predict future responses and individualize treatment for each patient. By analyzing parameter confidence intervals and confidence and prediction intervals for the state variables, we develop a parameter space reduction algorithm that results in better future response predictions for each individual patient. Moreover, use of another subset selection method, namely Structured Covariance Analysis, that …

Determinants Of Incidence And Hessian Matrices Arising From The Vector Space Lattice, Saeed Nasseh, Alexandra Seceleanu, Junzo Watanabe

Department of Mathematics: Faculty Publications

Let V = ⁿi= _o V^I bethe lattice of subspaces of the n-dimensional vector space over the finite field Fq, and let A be the graded Gorenstein algebra defined over Q which has V as a Q basis. Let F be the Macaulay dual generator for A. We explicitly compute the Hessian determinant j 2F= Xi Xj j, evaluated at the point X1 = X2 = ... = XN = 1, and relate it to the determinant of the incidence matrix between V1 and Vn-1. Our exploration is motivated by the fact that both of these matrices naturally …

Resilience For Asynchronous Iterative Methods For Sparse Linear Systems, Evan Coleman

Computational Modeling & Simulation Engineering Theses & Dissertations

Large scale simulations are used in a variety of application areas in science and engineering to help forward the progress of innovation. Many spend the vast majority of their computational time attempting to solve large systems of linear equations; typically arising from discretizations of partial differential equations that are used to mathematically model various phenomena. The algorithms used to solve these problems are typically iterative in nature, and making efficient use of computational time on High Performance Computing (HPC) clusters involves constantly improving these iterative algorithms. Future HPC platforms are expected to encounter three main problem areas: scalability of code, …

On Analytic Nonlinear Input-Output Systems: Expanded Global Convergence And System Interconnections, Irina M. Winter Arboleda

Electrical & Computer Engineering Theses & Dissertations

Functional series representations of nonlinear systems first appeared in engineering in the early 1950’s. One common representation of a nonlinear input-output system are Chen-Fliess series or Fliess operators. Such operators are described by functional series indexed by words over a noncommutative alphabet. They can be viewed as a noncommutative generalization of a Taylor series. A Fliess operator is said to be globally convergent when its radius of convergence is infinite, in other words, when there is no a priori upper bound on both the L1-norm of an admissible input and the length of time over which the corresponding output is …