Nuclear fuel cycles (NFC) are the collection of interconnected processes which generate electricity through nuclear power. Due to the high degree of coupling between components even in the simplest cycles, the need for a dynamic fuel cycle simulator and analysis framework arises. The work presented herein develops essential physics models of nuclear power reactors and incorporate them into a NFC simulation framework. First, a one-energy group reactor model is demonstrated. This essential physics model is then to simulate a sampling fuel cycles which are perturbations of well known base-case cycles. Because the NFC may now be simulated quickly, stochastically modeling many fuel cycle realizations dramatically expands the parameter space which may be analyzed. Finally, a multigroup reactor model which incorporates spectral changes as a function of burnup is presented to increase the fidelity of the original one-group reactor. These methods form a suite of modeling technologies which reach from the lowest levels (individual components) to the highest (inter-cycle comparisons). Prior to the development of this model suite, such broad-ranging analysis had been unrealistic to perform. The work here thus presents a new, multi-scale approach to fuel cycle system design. / text

The following graduate report is an overview of my artistic endeavors spanning the past three years at the University of Texas at Austin. While at UT, I have concentrated on making paintings that focus on the relationship between abstraction, representation, and entropy. Using banal, often overlooked cultural objects as subject matter, I paint ambiguous scenes that teeter between disintegration and formation. Representations of banal detritus within an ambiguous natural space become a metaphor for memory, culture, and life and death alluding to unseen forces and, ultimately, a lack of control. Using a combination of random and deliberate decisions, I aim to create a commentary about the unpredictable yet conformist aspects of the world in which we participate. / text

This dissertation addresses probabilistic inference via relative entropy minimization subject to expectation constraints. A canonical representation of the solution is determined without the requirement for convexity of the constraint set, and is given by members of an exponential family. The use of conjugate priors for relative entropy minimization is proposed, and a class of conjugate prior distributions is introduced. An alternative representation of the solution is provided as members of the prior family when the prior distribution is conjugate. It is shown that the solutions can be found by direct optimization with respect to members of such parametric families. Constrained Bayesian inference is recovered as a special case with a specific choice of constraints induced by observed data. The framework is applied to the development of novel probabilistic models for multitask learning subject to constraints determined by domain expertise. First, a model is developed for multitask learning that jointly learns a low rank weight matrix and the prior covariance structure between different tasks. The multitask learning approach is extended to a class of nonparametric statistical models for transposable data, incorporating side information such as graphs that describe inter-row and inter-column similarity. The resulting model combines a matrix-variate Gaussian process prior with inference subject to nuclear norm expectation constraints. In addition, a novel nonparametric model is proposed for multitask bipartite ranking. The proposed model combines a hierarchical matrix-variate Gaussian process prior with inference subject to ordering constraints and nuclear norm constraints, and is applied to disease gene prioritization. In many of these applications, the solution is found to be unique. Experimental results show substantial performance improvements as compared to strong baseline models. / text

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Melting point finds applications in chemical identification, purification and in the calculation of a number of other physicochemical properties such as vapor pressure and aqueous solubility. Despite the availability of enormous amounts of experimental data, no generally applicable methods have been developed to estimate the melting point of a compound from its chemical structure. A quick estimation of melting point can be a useful tool in the design of new chemical entities.In this dissertation, a simple means of estimating the melting points for a large variety of pharmaceutically and environmentally relevant organic compounds is developed. Melting points are predicted from the separate calculation of the enthalpy and entropy of melting directly from the chemical structure. The entropy of melting is calculated using a semi-empirical equation based on only two non-additive molecular parameters. This equation is validated and refined using a large collection of experimental entropy of melting values. The enthalpy of melting is calculated by additive group contributions.Melting points are estimated from the ratio of the enthalpy of melting and the entropy of melting. All of the methods and group contributions developed in this study are compatible with the UPPER (Unified Physical Property Estimating Relationships) scheme. The predicted melting points are compared to experimental melting points for over 2200 organic compounds collected from the literature. The average absolute error in melting point prediction is 30.1 °. This is a very reasonable estimate considering the size and diversity of the dataset used in this study.

Information entropy parameter has been applied for an expression of the result of data assessment and it is supplemented by an index of sample of data that was evaluated out of set of information. A modelling system and software have been developed that can be used and are used for practical processing of measurement data for circular raster scales. / Duomenų įverčiui išreikšti pritaikytas informacinės entropijos parametras pateiktoje rezultato išraiškoje yra papildytas rodikliu apie duomenų imtį, kuri buvo įvertinta iš visos šį objektą charakterizuojančių duomenų aibės. Sukurta modeliavimo sistema ir programinė įranga gali būti naudojama didelio skaičiaus nežinomųjų lygtims spręsti, o praktikoje naudojama rastrinių skalių matavimo duomenims apdoroti.

The maximum entropy moment closure for the two-moment approximation of the radiative transfer equation is presented. The resulting moment equations, known as the M1 model, are solved using a finite-volume method with adaptive mesh refinement (AMR) and two Riemann-solver based flux function solvers: a Roe-type and a Harten-Lax van Leer (HLL) solver. Three different boundary schemes are also presented and discussed. When compared to the discrete ordinates method (DOM) in several representative one- and two-dimensional radiation transport problems, the results indicate that while the M1 model cannot accurately resolve multi-directional radiation transport occurring in low-absorption media, it does provide reasonably accurate solutions, both qualitatively and quantitatively, when compared to the DOM predictions in most of the test cases involving either absorbing-emitting or scattering media. The results also show that the M1 model is computationally less expensive than DOM for more realistic radiation transport problems involving scattering and complex geometries.

The maximum entropy moment closure for the two-moment approximation of the radiative transfer equation is presented. The resulting moment equations, known as the M1 model, are solved using a finite-volume method with adaptive mesh refinement (AMR) and two Riemann-solver based flux function solvers: a Roe-type and a Harten-Lax van Leer (HLL) solver. Three different boundary schemes are also presented and discussed. When compared to the discrete ordinates method (DOM) in several representative one- and two-dimensional radiation transport problems, the results indicate that while the M1 model cannot accurately resolve multi-directional radiation transport occurring in low-absorption media, it does provide reasonably accurate solutions, both qualitatively and quantitatively, when compared to the DOM predictions in most of the test cases involving either absorbing-emitting or scattering media. The results also show that the M1 model is computationally less expensive than DOM for more realistic radiation transport problems involving scattering and complex geometries.

<p>In this thesis, I develop two sets of methods to help understand two distinct but also</p><p>related issues in financial economics.</p><p>First, representative agent models have been successfully applied to explain asset</p><p>market phenomenons. They are often simple to work with and appeal to intuition by</p><p>permitting a direct link between the agent's optimization behavior and asset market</p><p>dynamics. However, their particular modeling choices sometimes yield undesirable</p><p>or even counterintuitive consequences. Several diagnostic tools have been developed by the asset pricing literature to detect these unwanted consequences. I contribute to this literature by developing a new continuum of nonparametric asset pricing bounds to diagnose representative agent models. Chapter 1 lays down the theoretical framework and discusses its relevance to existing approaches. Empirically, it uses bounds implied by index option returns to study a well-known class of representative agent models|the rare disaster models. Chapter 2 builds on the insights of Chapter 1 to study dynamic models. It uses model implied conditional variables to sharpen asset pricing bounds, allowing a more powerful diagnosis of dynamic models.</p><p>While the first two chapters focus on the diagnosis of a particular model, Chapter</p><p>3 and 4 study the joint inference of a group of models or risk factors. Drawing on</p><p>multiple hypothesis testing in the statistics literature, Chapter 3 shows that many of</p><p>the risk factors documented by the academic literature are likely to be false. It also</p><p>proposes a new statistical framework to study multiple hypothesis testing under test</p><p>correlation and hidden tests. Chapter 4 further studies the statistical properties of</p><p>this framework through simulations.</p> / Dissertation