PhD.

Delay differential equations (DDEs) are a class of differential equations that have received considerable recent attention and been shown to model many real life problems, traditionally formulated as systems of ordinary differential equations (ODEs), more naturally and more accurately. Ideally a DDE modeling package should provide facilities for approximating the solution, performing a sensitivity analysis and estimating unknown parameters. In this thesis we propose new techniques for efficient simulation, accurate sensitivity analysis and reliable parameter estimation of DDEs. We propose a new framework for designing a delay differential equation (DDE) solver which works with any supplied initial value problem (IVP) solver that is based on a general linear method (GLM) and can provide dense output. This is done by treating a general DDE as a special example of a discontinuous IVP. We identify a precise process for the numerical techniques used when solving the implicit equations that arise on a time step, such as when the underlying IVP solver is implicit or the delay vanishes. We introduce an equation governing the dynamics of sensitivities for the most general system of parametric DDEs. Then, having a similar view as the simulation (DDEs as discontinuous ODEs), we introduce a formula for finding the size of jumps that appear at discontinuity points when the sensitivity equations are integrated. This leads to an algorithm which can compute sensitivities for various kind of parameters very accurately. Finally, we discuss the structure of our evolving modeling package DDEM. We present a process that has been used for incorporating existing codes to reduce the implementation time. We discuss the object-oriented paradigm as a way of having a manageable design with reusable and customizable components. The package is programmed in C++ and provides a user-friendly calling sequences. The numerical results are very encouraging and show the effectiveness of the techniques. We also develop an algorithm for reliable parameter identification of DDEs. We propose a method for adding extra constraints to the optimization problem, changing a possibly non-smooth optimization to a smooth problem. These constraints are effectively handled using information from the simulator and the sensitivity analyzer.

This book constitutes an up-to-date account of principles, methods, and tools for mathematical and statistical modelling in a wide range of research fields, including medicine, health sciences, biology, environmental science, engineering, physics, chemistry, computation, finance, economics, and social sciences. It presents original solutions to real-world problems, emphasizes the coordinated development of theories and applications, and promotes interdisciplinary collaboration among mathematicians, statisticians, and researchers in other disciplines. Based on a highly successful meeting, the International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019, held from August 18 to 23, 2019, on the main campus of Wilfrid Laurier University, Waterloo, Canada, the contributions are the results of submissions from the conference participants. They provide readers with a broader view of the methods, ideas and tools used in mathematical, statistical and computational sciences.

This book provides an understandable introduction to one approach to design sensitivity computation and illustrates some of the important mathematical and computational issues inherent in using the sensitivity equation method (SEM) for partial differential equations. The authors use basic models to illustrate the computational issues that one might encounter when applying the SEM in a laboratory or research setting, while providing an overview of applications and computational issues regarding sensitivity calculations performed by way of continuous sensitivity equation methods (CSEM).

1. Introduction. 1.1. Motivation. 1.2. Background. 1.3. Scope of this document. 1.4. Original contributions -- 2. Solutions of systems of DDEs via the matrix Lambert W function. 2.1. Introduction. 2.2. Free systems of DDEs. 2.3. Forced systems. 2.4. Approach using the Laplace transformation. 2.5. Concluding remarks -- 3. Stability of systems of DDEs via the Lambert W function with application to machine tool chatter. 3.1. Introduction. 3.1. The Chatter equation in the turning process. 3.3. Solving DDEs and stability. 3.4. Concluding remarks -- 4. Controllability and observability of systems of linear delay differential equations via the matrix Lambert W function. 4.1. Introduction. 4.2. Controllability. 4.3. Observability. 4.4. Illustrative example. 4.5. Conclusions and future work -- 5. Eigenvalue assignment via the Lambert W function for control of time-delay systems. 5.1. Introduction. 5.2. Eigenvalue assignment for time-delay systems. 5.3. Design of a feedback Controller. 5.4. Conclusions -- 6. Robust control and time-domain specifications for systems of delay differential equations via eigenvalue assignment. 6.1. Introduction. 6.2. Robust feedback. 6.3. Time-domain specifications. 6.4. Concluding remarks -- 7. Design of observer-based feedback control for time-delay systems with application to automotive powertrain control. 7.1. Introduction. 7.2. Problem formulation. 7.3. Design of observer-based feedback controller. 7.4. Application to diesel engine control. 7.5. Conclusions -- 8. Eigenvalues and sensitivity analysis for a model of HIV pathogenesis with an intracellular delay. 8.1. Introduction. 8.2. HIV pathogenesis dynamic model with an intracellular delay. 8.3. Rightmost eigenvalue analysis. 8.4. Sensitivity analysis. 8.5. Concluding remarks and future work