A recursive-iterative algorithm is developed for predicting nonlinear viscoelastic behaviors of isotropic materials that belong to the thermorheologically complex material (TCM). The algorithm is derived based on implicit stress integration solutions within a general displacement based FE structural analyses for small deformations and uncoupled thermo-mechanical problems. A previously developed recursive-iterative algorithm for a stress-dependent hereditary integral model which was developed by Haj-Ali and Muliana is modified to include time-temperature effects. The recursive formula allows bypassing the need to store entire strain histories at each Gaussian integration point. Two types of iterative procedures, which are fixed point and Newton-Raphson methods, are examined within the recursive algorithm. Furthermore, a consistent tangent stiffness matrix is formulated to accelerate convergence and avoid divergence. The efficiency and accuracy of the proposed algorithm are evaluated using available experimental data and several structural analyses. The performance of the proposed algorithm under multi-axial conditions is verified with analytical solutions of creep responses of a plate with a hole. Next, the recursive-iterative algorithm is used to predict the overall response of single lap-joint. Numerical simulations of time-dependent crack propagations of adhesive bonded joints are also presented. For this purpose, the recursive algorithm is implemented in cohesive elements. The numerical assessment of the TCM and thermorheologically simple material (TSM) behaviors has also been performed. The result showed that TCM are able to describe thermo-viscoelastic behavior under general loading histories, while TSM behaviors are limited to isothermal conditions. The proposed numerical algorithm can be easily used in a micromechanical model for predicting the overall composite responses. Examples are shown for solid spherical particle reinforced composites. Detailed unit-cell FE models of the composite systems are generated to verify the capability of the above micromechanical model for predicting the overall nonlinear viscoelastic behaviors.

Data-mining has become a hot topic in recent years. It consists of extracting relevant information or structures from data such as: pictures, textual material, networks, etc. Such information or structures are usually not trivial to obtain and many techniques have been proposed to address this problem, including Independent Component Analysis, Latent Sematic Analysis, etc. Nonnegative Matrix Factorization is yet another technique that relies on the nonnegativity of the data and the nonnegativity assumption of the underlying model. The main advantage of this technique is that nonnegative objects are modeled by a combination of some basic nonnegative parts, which provides a physical interpretation of the construction of the objects. This is an exclusive feature that is known to be useful in many areas such as Computer Vision, Information Retrieval, etc. In this thesis, we look at several aspects of Nonnegative Matrix Factorization, focusing on numerical algorithms and their applications to different kinds of data and constraints. This includes Tensor Nonnegative Factorization, Weighted Nonnegative Matrix Factorization, Symmetric Nonnegative Matrix Factorization, Stochastic Matrix Approximation, etc. The recently proposed Rank-one Residue Iteration (RRI) is the common thread in all of these factorizations. It is shown to be a fast method with good convergence properties which adapts well to many situations.

In this paper, we discuss the need for quantum error correction. We also describe some basic techniques used in quantum error correction which includes decoherence-free subspaces and subsystems. These subspaces and subsystems are described in detail. We also introduce a numerical algorithm that was used previously to search for these decoherence-free subspaces and subsystems under collective error. It is useful to search for them as they can be used to store quantum information. We use this algorithm in some specific examples involving qubits and qutrits. The results of these algorithm are then compared with the error algebra obtained using Young tableaux. We use these results to describe how the specific numerical algorithm can be used for the search of approximate decoherence-free subspaces and subsystems and minimal noise subsystems.

This dissertation develops and analyzes differential equation-based mathematical models and efficient numerical methods and algorithms for genetic regulatory network identification. The primary objectives of the dissertation are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets using the approach based on differential equation modeling. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes a biological energy functional. The variational principle serves not only as a selection criterion to pick up the right biological solution of the underlying differential equation model but also provide an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed in the dissertation. Model validations using both synthetic network datasets and real world subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. Coli are done on all five proposed variational models and a performance comparison vs some existing genetic regulatory network identification methods is also provided. As microarray data is typically noisy, in order to take into account the noise effect in the mathematical models, we propose a new approach based on stochastic differential equation modeling and generalize the deterministic variational framework to a stochastic variational framework which relies on stochastic optimization. Numerical algorithms are also proposed for computing solutions of the stochastic variational models. To address the important issue of post-processing computed networks to reflect the small-world property of underlying genetic regulatory networks, a novel threshholding technique based on the Random Matrix Theory is proposed and tested on various synthetic network datasets.

The overarching goal of this dissertation research is to realize a virtual collaboratory for the investigation of large-scale scientific computing applications which generally experience different execution phases at runtime and each phase has different computational, communication and storage requirements as well as different physical characteristics. Consequently, an optimal solution or numerical scheme for one execution phase might not be appropriate for the next phase of the application execution. Choosing the ideal numerical algorithms and solutions for all application runtime phases remains an active research area. In this dissertation, we present Physics Aware Programming (PAP) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Runtime Manager (PARM) to exploit the PAP paradigm. PARM periodically monitors and analyzes the runtime characteristics of the application to identify its current execution phase (state). For each change in the application execution phase, PARM will adaptively exploit the spatial and temporal attributes of the application in the current state to identify the ideal numerical algorithms/solvers that optimize its performance. We have evaluated our approach using a real world application (Variable Saturated Aquifer Flow and Transport (VSAFT2D)) commonly used in subsurface modeling, diffusion problem kernel and seismic problem kernel. We evaluated the performance gain of the PAP paradigm with up to 2,000,000 nodes in the computation domain implemented on 32 processors. Our experimental results show that by exploiting the application physics characteristics at runtime and applying the appropriate numerical scheme with adapted spatial and temporal attributes, a significant speedup can be achieved (around 80%) and the overhead injected by PAP is negligible (less than 2%). We also show that the results using PAP is as accurate as the numerical solutions that use fine grid resolution.

This thesis presents a simulation model for the dynamics of the human hand for application to an anthropomorphic prosthesis. The Articulated Body Algorithm (ABA) algorithm was selected to model the dynamics of a tree type robotic structure. The ABA is a numerical Newton-Euler based algorithm that solves the forward dynamics (obtaining the joint accelerations from the applied torques and forces) for a joint-link model. The main advantage of this algorithm resides in the analysis of the system link by link rather than the entire system analysis. This feature enables the implementation of a computationally efficient code and makes the algorithm generic enough to be applied to almost any robotic structure, with minimal additional effort. Furthermore, as the basic algorithm only handles serial structures, it was modified to include the effect of the gravity, loads on the end-effector, elasticity and damping at the joints, the generalization to tree-type structures, and, finally, the inclusion of impact analysis.

This thesis presents a simulation model for the dynamics of the human hand for application to an anthropomorphic prosthesis. The Articulated Body Algorithm (ABA) algorithm was selected to model the dynamics of a tree type robotic structure. The ABA is a numerical Newton-Euler based algorithm that solves the forward dynamics (obtaining the joint accelerations from the applied torques and forces) for a joint-link model. The main advantage of this algorithm resides in the analysis of the system link by link rather than the entire system analysis. This feature enables the implementation of a computationally efficient code and makes the algorithm generic enough to be applied to almost any robotic structure, with minimal additional effort. Furthermore, as the basic algorithm only handles serial structures, it was modified to include the effect of the gravity, loads on the end-effector, elasticity and damping at the joints, the generalization to tree-type structures, and, finally, the inclusion of impact analysis.

We introduce new methods based on the L2 Optimal Transport (OT) problem and the Navier-Stokes equations to approximate a fluid velocity field from images obtained with Particle Image Velocimetry (PIV) measurements. The main idea is to consider two successive images as the initial and final densities in the OT problem, and to use the associated OT flow as an estimate of the underlying physical flow. We build a simple but realistic model for PIV data, and use it to analyze the behavior of the transport map in this situation. We then design and implement a series of post-processing filters created to improve the quality of the numerical results, and we establish comparisons with traditional cross-correlation algorithms. These results indicate that the OT-PIV procedure performs well on low to medium seeding densities, and that it gives better results than typical cross-correlation algorithms in some cases. Finally, we use a variational method to project the OT velocity field on the space of solutions of the Navier-Stokes equations, and extend it to the rest of the fluid domain, outside the particle locations. This extension provides an effective filtering of the OT solution beyond the local post-processing filters, as demonstrated by several numerical experiments. / Graduate

In clinical research, it is sometimes desirable to dichotomize a continuous variable so that the information expressed using a dichotomous variable is more straightforward for clinicians to interpret and communicate. The distribution of a continuous variable can differ between two populations defined by the case status. Under such a scenario, the dichotomization process can be based on distributions of the continuous variable in two distinct populations. The resulting dichotomous variable can be used as an endpoint in future studies. Even though dichotomization has not been extensively studied, dichotomization has been commonly carried out in clinical trials. We developed a methodology on dichotomization based on maximizing the correlation between the two populations and the dichotomous variable. We have investigated several commonly assumed distributions (e.g., normal, log-normal and gamma distribution) of the continuous variable for the two populations and developed a numerical algorithm for the proposed method to determine the optimal cutoff point. The two populations can differ in form and/or parameters. The proposed method of finding the optimal cutoff was also extended to adjust for covariates. In real world scenarios where the two samples from the two populations are not completely identified, we recommended using the EM method to first estimate the parameters associated with the two populations before applying the proposed method to find the optimal cutoff point. The performance of the proposed method with the numerical algorithm and the EM method has been studied for several theoretical distributions and using simulated data. These methods were also applied to a varicella vaccine example. / Statistics

<p> This study focuses on developing a new online recursive numerical algorithm for a coupled nonlinear inverse heat conduction-microstructure problem. This algorithm is essential in identifying, designing and controlling many industrial applications such as the quenching process for heat treating of materials, chemical vapor deposition and industrial baking. In order to develop the above algorithm, a systematic four stage research plan has been conducted. </P> <p> The first and second stages were devoted to thoroughly reviewing the existing inverse heat conduction techniques. Unlike most inverse heat conduction solution methods that are batch form techniques, the online input estimation algorithm can be used for controlling the process in real time. Therefore, in the first stage, the effect of different parameters of the online input estimation algorithm on the estimate bias has been investigated. These parameters are the stabilizing parameter, the measurement errors standard deviation, the temporal step size, the spatial step size, the location of the thermocouple as well as the initial assumption of the state error covariance and error covariance of the input estimate. Furthermore, three different discretization schemes; namely: explicit, implicit and Crank-Nicholson have been employed in the input estimation algorithm to evaluate their effect on the algorithm performance. </p> <p> The effect of changing the stabilizing parameter has been investigated using three different forms of boundary conditions covering most practical boundary heat flux conditions. These cases are: square, triangular and mixed function heat fluxes. The most important finding of this investigation is that a robust range of the stabilizing parameter has been found which achieves the desired trade-off between the filter tracking ability and its sensitivity to measurement errors. For the three considered cases, it has been found that there is a common optimal value of the stabilizing parameter at which the estimate bias is minimal. This finding is important for practical applications since this parameter is usually unknown. Therefore, this study provides a needed guidance for assuming this parameter. </p> <p> In stage three of this study, a new, more efficient direct numerical algorithm has been developed to predict the thermal and microstructure fields during quenching of steel rods. The present algorithm solves the full nonlinear heat conduction equation using a central finite-difference scheme coupled with a fourth-order Runge-Kutta nonlinear solver. Numerical results obtained using the present algorithm have been validated using experimental data and numerical results available in the literature. In addition to its accurate predictions, the present algorithm does not require iterations; hence, it is computationally more efficient than previous numerical algorithms. </p> <p> The work performed in stage four of this research focused on developing and applying an inverse algorithm to estimate the surface temperatures and surface heat flux of a steel cylinder during the quenching process. The conventional online input estimation algorithm has been modified and used for the first time to handle this coupled nonlinear problem. The nonlinearity of the problem has been treated explicitly which resulted in a non-iterative algorithm suitable for real-time control of the quenching process. The obtained results have been validated using experimental data and numerical results obtained by solving the direct problem using the direct solver developed in stage three of this work. These results showed that the algorithm is efficiently reconstructing the shape of the convective surface heat flux. </p> / Thesis / Doctor of Philosophy (PhD)