In this work, we investigate input-to-state stability (ISS) and other related stability properties for control systems with time-delays. To overcome the complexity caused by the presence of the delays, we adopt a Razumikhin approach. The underlying idea of this approach is to treat the delayed variables as system uncertainties. The advantage of this approach is that one works in the more familiar territory of stability analysis for delay-free systems in the context of ISS instead of carrying out stability analysis on systems of functional differential equations. Our first step is to provide criteria on ISS and input-to-input stability properties based on the Razumikhin approach. We then turn our attention to large-scale interconnected systems. It has been well recognized that the small-gain theory is a powerful tool for stability analysis of interconnected systems. Using the Razumikhin approach, we develop small-gain theorems for interconnected systems consisting of two or more subs ystems with time-delays present either in the interconnection channels or within the subsystems themselves. As an interesting application, we apply our results to an existing model for hematopoesis, a blood cell production process,and improve the previous results derived by linear methods. Another important stability notion in the framework of ISS is the integral ISS (iISS) property. This is a weaker property than ISS, so it supplies to a larger class of systems. As in the case of ISS, we provide Razumikhin criteria on iISS for systems with delays. An example is presented to illustrate that though very useful in practice, the Razumikhin approach only provides sufficient conditions, not equivalent conditions. Finally, we address stability of time-varying systems with delays in the framework of ISS. / In particular, we consider Lyapunov-Razumikhin functions whose decay rates are affected by time-varying functions that can be zero or even negative on some sets of non-zero measure. Our motivation is that it is often less demanding to find or construct such a Lyapunov function than one with a uniform decay rate. We also extend our small-gain theorems to the time-varying case by treating the time-varying system as an auxiliary time-invariant system. / Shanaz Tiwari. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography and index. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

O método aqui desenvolvido, bem como as aplicações feitas ao estudo de sistemas clássicos da dinâmica não-linear, tiveram por objetivo construir uma ferramenta adequada à descrição das características globais de fenômenos complexos da dinâmica não-linear. Uma característica típica da descrição probabilística do comportamento dinâmico de um sistema é sua expressão em termos da evolução temporal da função densidade de probabilidade dos estados, que é governada por uma equação diferencial linear, em contraste com a descrição temporal convencional, utilizada em dinâmica não-linear. Enquanto esta última, comumente dita determinística, exibe fenômenos tais como instabilidades, bifurcações, sensibilidade a condições iniciais etc, a descrição probabilística se manifesta, quando o sistema dinâmico detém propriedades de ergodicidade, em uma evolução não-reversível da função densidade de probabilidade em direção a um estado final invariante, mais especificamente tendendo ao equilíbrio global de um sistema linear. Este trabalho visa a aplicação da teoria probabilística da evolução de densidades de probabilidade em um problema de capotamento de veículos. Para isso, a teoria é descrita por meio de seus fundamentos e aplicada primeiramente em modelos clássicos da dinâmica não-linear, que, por serem bem estudados, podem comprovar a validade, bem como a extensão dessa forma de análise. / The method developed in this work, as well as its application in classical non-linear dynamics systems, had the main purpose of building a suitable tool in describing global complex phenomena of non-linear dynamics. A typical feature of the probabilistic approach of dynamics systems behavior is the ability to express it as a temporal probability density function evolution in terms of a linear evolution equation, which is ruled by a linear differential equation, as opposed to the regular temporal description used in non-linear dynamics. While the aforementioned description, also called deterministic, may face a variety of phenomena such as instabilities, bifurcation, high sensibility to initial conditions etc, in the probabilistic approach, as long as the dynamic system enjoys some ergodic properties, the probability density function will be driven irreversibly to a final invariant state, towards a global equilibrium of a linear system. This work consists in the application of probabilistic theory of density evolution in the problem of vehicle rollover subject to a certain maneuver. In order to accomplish that, all theory described is firstly applied to classical problems of nonlinear dynamics, since they have many established results, and as such, can validate and extend this sort of analysis for any dynamic system.

Technological advances in the recent decades have enabled cancer researchers to probe the disease at multiple resolutions. This wealth of experimental data combined with computational systems biology methods is now leading to predictive models of cancer progression and response to therapy. We begin by presenting our research group’s multis-cale in silico framework for modeling cancer, whose core is a tissue-scale computational model capable of tracking the progression of tumors from a diffusion-limited avascular phase through angiogenesis, and into invasive lesions with realistic, complex morphologies. We adapt this core model to consider the delivery of systemically-administered anticancer agents and their effect on lesions once they reach their intended nuclear target. We calibrate the model parameters using in vitro data from the literature, and demonstrate through simulation that transport limitations affecting drug and oxygen distributions play a significant role in hampering the efficacy of chemotherapy; a result that has since been validated by in vitro experimentation. While this study demonstrates the capability of our adapted core model to predict distributions (e.g., cell density, pressure, oxygen, nutrient, drug) within lesions and consequent tumor morphology, nevertheless, the underlying factors driving tumor-scale behavior occur at finer scales. What is needed in our multi-scale approach is to parallel reality, where molecular signaling models predict cellular behavior, and ultimately drive what is seen at the tumor level. Models of signaling pathways linked to cell models are already beginning to surface in the literature. We next transition our research to the molecular level, where we employ data mining and bioinformatics methods to infer signaling relationships underlying a subset of breast cancer that might benefit from targeted therapy of Androgen Receptor and associated pathways. Defining the architecture of signaling pathways is a critical first step towards development of pathways models underlying tumor models, while also providing valuable insight for drug discovery. Finally, we develop an agent-based, cell-scale model focused on predicting motility in response to chemical signals in the microenvironment, generally accepted to be a necessary feature of cancer invasion and metastasis. This research demonstrates the use of signaling models to predict emergent cell behavior, such as motility. The research studies presented in this dissertation are critical steps towards developing a predictive, in silico computational model for cancer progression and response to therapy. Our Laboratory for Computational & Predictive Oncology, in collaboration with research groups throughout in the United States and Europe are following a computational systems biology paradigm where model development is fueled by biological knowledge, and model predictions are refining experimental focus. The ultimate objective is a virtual cancer simulator capable of accurately simulating cancer progression and response to therapy on a patient-specific basis. / text

Not available / text

Not available / text

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 216-228.

Thesis (M. Eng.)--Carleton University, 2001. / Includes bibliographical references (p. 82-84). Also available in electronic format on the Internet.

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed October 24, 2007) Includes bibliographical references.

O método aqui desenvolvido, bem como as aplicações feitas ao estudo de sistemas clássicos da dinâmica não-linear, tiveram por objetivo construir uma ferramenta adequada à descrição das características globais de fenômenos complexos da dinâmica não-linear. Uma característica típica da descrição probabilística do comportamento dinâmico de um sistema é sua expressão em termos da evolução temporal da função densidade de probabilidade dos estados, que é governada por uma equação diferencial linear, em contraste com a descrição temporal convencional, utilizada em dinâmica não-linear. Enquanto esta última, comumente dita determinística, exibe fenômenos tais como instabilidades, bifurcações, sensibilidade a condições iniciais etc, a descrição probabilística se manifesta, quando o sistema dinâmico detém propriedades de ergodicidade, em uma evolução não-reversível da função densidade de probabilidade em direção a um estado final invariante, mais especificamente tendendo ao equilíbrio global de um sistema linear. Este trabalho visa a aplicação da teoria probabilística da evolução de densidades de probabilidade em um problema de capotamento de veículos. Para isso, a teoria é descrita por meio de seus fundamentos e aplicada primeiramente em modelos clássicos da dinâmica não-linear, que, por serem bem estudados, podem comprovar a validade, bem como a extensão dessa forma de análise. / The method developed in this work, as well as its application in classical non-linear dynamics systems, had the main purpose of building a suitable tool in describing global complex phenomena of non-linear dynamics. A typical feature of the probabilistic approach of dynamics systems behavior is the ability to express it as a temporal probability density function evolution in terms of a linear evolution equation, which is ruled by a linear differential equation, as opposed to the regular temporal description used in non-linear dynamics. While the aforementioned description, also called deterministic, may face a variety of phenomena such as instabilities, bifurcation, high sensibility to initial conditions etc, in the probabilistic approach, as long as the dynamic system enjoys some ergodic properties, the probability density function will be driven irreversibly to a final invariant state, towards a global equilibrium of a linear system. This work consists in the application of probabilistic theory of density evolution in the problem of vehicle rollover subject to a certain maneuver. In order to accomplish that, all theory described is firstly applied to classical problems of nonlinear dynamics, since they have many established results, and as such, can validate and extend this sort of analysis for any dynamic system.

A defining feature of quantum many-body systems is the presence of entanglement among their constituents. Besides providing valuable insights on several physical properties, entanglement is also responsible for the computational complexity of simulating quantum systems with variational methods. This thesis explores several aspects of entanglement in many-body systems, with the primary goal of devising efficient approaches for the study of topological properties and quantum dynamics of lattice models. The first focus of this work is the development of variational wavefunctions inspired by artificial neural networks. These can efficiently encode long-range and extensive entanglement in their structure, as opposed to the case of tensor network states. This feature makes them promising tools for the study of topologically ordered phases, quantum critical states as well as dynamical properties of quantum systems. In this thesis, we characterize the representational power of a specific class of artificial neural network states, constructed from Boltzmann machines. First, we show that wavefunctions obtained from restricted Boltzmann machines can efficiently parametrize chiral topological phases, such as fractional quantum Hall states. We then turn our attention to deep Boltzmann machines. In this framework, we propose a new class of variational wavefunctions, coined generalized transfer matrix states, which encompass restricted Boltzmann machine and tensor network states. We investigate the entanglement properties of this ansatz, as well as its capability of representing physical states. Understanding how the entanglement properties of a system evolve in time is the second focus of this thesis. In this context, we first investigate the manifestation of topological properties in the unitary dynamics of systems after a quench, using the degeneracy of the entanglement spectrum as a possible signature. We then analyze the phenomenon of entanglement growth, which limits to short timescales the applicability of tensor network methods in out-of-equilibrium problems. We investigate whether these limitations can be overcome by exploiting the dependence of entanglement entropies on the chosen computational basis. Specifically, we study how the spreading of quantum correlations can be contained by means of time-dependent basis rotations of the state, using exact diagonalization to simulate its dynamics after a quench. Going beyond the case of sudden quenches, we then show how, in certain weakly interacting problems, the asymptotic value of the entanglement entropy can be tuned by modifying the velocity at which the parameters in the Hamiltonian are changed. This enables the simulation of longer timescales using tensor network approaches. We present preliminary results obtained with matrix product states methods, with the goal of studying how equilibration affects the transport properties of interacting systems at long times.