A probabilistic treatment of uncertainty in nonlinear dynamical systems

In this work, computationally efficient approximate methods are developed for analyzing uncertain dynamical systems. Uncertainties in both the excitation and the modeling are considered and examples are presented illustrating the accuracy of the proposed approximations.

For nonlinear systems under uncertain excitation, methods are developed to approximate the stationary probability density function and statistical quantities of interest. The methods are based on approximating solutions to the Fokker-Planck equation for the system and differ from traditional methods in which approximate solutions to stochastic differential equations are found. The new methods require little computational effort and examples are presented for which the accuracy of the proposed approximations compare favorably to results obtained by existing methods. The most significant improvements are made in approximating quantities related to the extreme values of the response, such as expected outcrossing rates, which are crucial for evaluating the reliability of the system.

Laplace's method of asymptotic approximation is applied to approximate the probability integrals which arise when analyzing systems with modeling uncertainty. The asymptotic approximation reduces the problem of evaluating a multidimensional integral to solving a minimization problem and the results become asymptotically exact as the uncertainty in the modeling goes to zero. The method is found to provide good approximations for the moments and outcrossing rates for systems with uncertain parameters under stochastic excitation, even when there is a large amount of uncertainty in the parameters. The method is also applied to classical reliability integrals, providing approximations in both the transformed (independently, normally distributed) variables and the original variables. In the transformed variables, the asymptotic approximation yields a very simple formula for approximating the value of SORM integrals. In many cases, it may be computationally expensive to transform the variables, and an approximation is also developed in the original variables. Examples are presented illustrating the accuracy of the approximations and results are compared with existing approximations.

The accuracy of Kirchhoff's approximation in describing the far field speckles produced by random self-affine fractal surfaces

Based on the rigorous formulation of integral equations for the propagations of light waves at the medium interface, we carry out the numerical solutions of the random light field scattered from self-affine fractal surface samples. The light intensities produced by the same surface samples are also calculated in Kirchhoff's approximation, and their comparisons with the corresponding rigorous results show directly the degree of the accuracy of the approximation. It is indicated that Kirchhoff's approximation is of good accuracy for random surfaces with small roughness value w and large roughness exponent alpha. For random surfaces with larger w and smaller alpha, the approximation results in considerable errors, and detailed calculations show that the inaccuracy comes from the simplification that the transmitted light field is proportional to the incident field and from the neglect of light field derivative at the interface.

Optimization of positions and currents of tokamak poloidal field coils using genetic algorithms

Plasma equilibrium geometry has a great influence on the confinement and magnetohydrodynamic stability in tokamaks. The poloidal field (PF) system of a tokamak should be optimized to support the prescribed plasma equilibrium geometry. In this paper, a genetic algorithm-based method is applied to solve the optimization of the positions and currents of tokamak PF coils. To achieve this goal, we first describe the free-boundary code EQT Based on the EQT code, a genetic algorithm-based method is introduced to the optimization. We apply this new method to the PF system design of the fusion-driven subcritical system and plasma equilibrium geometry optimization of the Experimental Advanced Superconducting Tokamak (EAST). The results indicate that the optimization of the plasma equilibrium geometry can be improved by using this method.

Multiscale model reduction methods for deterministic and stochastic partial differential equations

Partial differential equations (PDEs) with multiscale coefficients are very difficult to solve due to the wide range of scales in the solutions. In the thesis, we propose some efficient numerical methods for both deterministic and stochastic PDEs based on the model reduction technique.

For the deterministic PDEs, the main purpose of our method is to derive an effective equation for the multiscale problem. An essential ingredient is to decompose the harmonic coordinate into a smooth part and a highly oscillatory part of which the magnitude is small. Such a decomposition plays a key role in our construction of the effective equation. We show that the solution to the effective equation is smooth, and could be resolved on a regular coarse mesh grid. Furthermore, we provide error analysis and show that the solution to the effective equation plus a correction term is close to the original multiscale solution.

For the stochastic PDEs, we propose the model reduction based data-driven stochastic method and multilevel Monte Carlo method. In the multiquery, setting and on the assumption that the ratio of the smallest scale and largest scale is not too small, we propose the multiscale data-driven stochastic method. We construct a data-driven stochastic basis and solve the coupled deterministic PDEs to obtain the solutions. For the tougher problems, we propose the multiscale multilevel Monte Carlo method. We apply the multilevel scheme to the effective equations and assemble the stiffness matrices efficiently on each coarse mesh grid. In both methods, the $\KL$ expansion plays an important role in extracting the main parts of some stochastic quantities.

For both the deterministic and stochastic PDEs, numerical results are presented to demonstrate the accuracy and robustness of the methods. We also show the computational time cost reduction in the numerical examples.

Evaluation of Two Alternative Carbon Capture and Storage Technologies: A Stochastic Model

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Protein structure refinement algorithms

Protein structure prediction has remained a major challenge in structural biology for more than half a century. Accelerated and cost efficient sequencing technologies have allowed researchers to sequence new organisms and discover new protein sequences. Novel protein structure prediction technologies will allow researchers to study the structure of proteins and to determine their roles in the underlying biology processes and develop novel therapeutics.

Difficulty of the problem stems from two folds: (a) describing the energy landscape that corresponds to the protein structure, commonly referred to as force field problem; and (b) sampling of the energy landscape, trying to find the lowest energy configuration that is hypothesized to be the native state of the structure in solution. The two problems are interweaved and they have to be solved simultaneously. This thesis is composed of three major contributions. In the first chapter we describe a novel high-resolution protein structure refinement algorithm called GRID. In the second chapter we present REMCGRID, an algorithm for generation of low energy decoy sets. In the third chapter, we present a machine learning approach to ranking decoys by incorporating coarse-grain features of protein structures.

Continuous stochastic processes

A general review of stochastic processes is given in the introduction; definitions, properties and a rough classification are presented together with the position and scope of the author's work as it fits into the general scheme.

The first section presents a brief summary of the pertinent analytical properties of continuous stochastic processes and their probability-theoretic foundations which are used in the sequel.

The remaining two sections (II and III), comprising the body of the work, are the author's contribution to the theory. It turns out that a very inclusive class of continuous stochastic processes are characterized by a fundamental partial differential equation and its adjoint (the Fokker-Planck equations). The coefficients appearing in those equations assimilate, in a most concise way, all the salient properties of the process, freed from boundary value considerations. The writer’s work consists in characterizing the processes through these coefficients without recourse to solving the partial differential equations.

First, a class of coefficients leading to a unique, continuous process is presented, and several facts are proven to show why this class is restricted. Then, in terms of the coefficients, the unconditional statistics are deduced, these being the mean, variance and covariance. The most general class of coefficients leading to the Gaussian distribution is deduced, and a complete characterization of these processes is presented. By specializing the coefficients, all the known stochastic processes may be readily studied, and some examples of these are presented; viz. the Einstein process, Bachelier process, Ornstein-Uhlenbeck process, etc. The calculations are effectively reduced down to ordinary first order differential equations, and in addition to giving a comprehensive characterization, the derivations are materially simplified over the solution to the original partial differential equations.

In the last section the properties of the integral process are presented. After an expository section on the definition, meaning, and importance of the integral process, a particular example is carried through starting from basic definition. This illustrates the fundamental properties, and an inherent paradox. Next the basic coefficients of the integral process are studied in terms of the original coefficients, and the integral process is uniquely characterized. It is shown that the integral process, with a slight modification, is a continuous Markoff process.

The elementary statistics of the integral process are deduced: means, variances, and covariances, in terms of the original coefficients. It is shown that an integral process is never temporally homogeneous in a non-degenerate process.

Finally, in terms of the original class of admissible coefficients, the statistics of the integral process are explicitly presented, and the integral process of all known continuous processes are specified.

Ab initio study of anharmonicity in high pressure Cmca-4 metallic hydrogen

Hydrogen is the only atom for which the Schr odinger equation is solvable. Consisting only of a proton and an electron, hydrogen is the lightest element and, nevertheless, is far from being simple. Under ambient conditions, it forms diatomic molecules H2 in gas phase, but di erent temperature and pressures lead to a complex phase diagram, which is not completely known yet. Solid hydrogen was rst documented in 1899 [1] and was found to be isolating. At higher pressures, however, hydrogen can be metallized. In 1935 Wigner and Huntington predicted that the metallization pressure would be 25 GPa [2], where molecules would disociate to form a monoatomic metal, as alkali metals that lie below hydrogen in the periodic table. The prediction of the metallization pressure turned out to be wrong: metallic hydrogen has not been found yet, even under a pressure as high as 320 GPa. Nevertheless, extrapolations based on optical measurements suggest that a metallic phase may be attained at 450 GPa [3]. The interest of material scientist in metallic hydrogen can be attributed, at least to a great extent, to Ashcroft, who in 1968 suggested that such a system could be a hightemperature superconductor [4]. The temperature at which this material would exhibit a transition from a superconducting to a non-superconducting state (Tc) was estimated to be around room temperature. The implications of such a statement are very interesting in the eld of astrophysics: in planets that contain a big quantity of hydrogen and whose temperature is below Tc, superconducting hydrogen may be found, specially at the center, where the gravitational pressure is high. This might be the case of Jupiter, whose proportion of hydrogen is about 90%. There are also speculations suggesting that the high magnetic eld of Jupiter is due to persistent currents related to the superconducting phase [5]. Metallization and superconductivity of hydrogen has puzzled scientists for decades, and the community is trying to answer several questions. For instance, what is the structure of hydrogen at very high pressures? Or a more general one: what is the maximum Tc a phonon-mediated superconductor can have [6]? A great experimental e ort has been carried out pursuing metallic hydrogen and trying to answer the questions above; however, the characterization of solid phases of hydrogen is a hard task. Achieving the high pressures needed to get the sought phases requires advanced technologies. Diamond anvil cells (DAC) are commonly used devices. These devices consist of two diamonds with a tip of small area; for this reason, when a force is applied, the pressure exerted is very big. This pressure is uniaxial, but it can be turned into hydrostatic pressure using transmitting media. Nowadays, this method makes it possible to reach pressures higher than 300 GPa, but even at this pressure hydrogen does not show metallic properties. A recently developed technique that is an improvement of DAC can reach pressures as high as 600 GPa [7], so it is a promising step forward in high pressure physics. Another drawback is that the electronic density of the structures is so low that X-ray di raction patterns have low resolution. For these reasons, ab initio studies are an important source of knowledge in this eld, within their limitations. When treating hydrogen, there are many subtleties in the calculations: as the atoms are so light, the ions forming the crystalline lattice have signi cant displacements even when temperatures are very low, and even at T=0 K, due to Heisenberg's uncertainty principle. Thus, the energy corresponding to this zero-point (ZP) motion is signi cant and has to be included in an accurate determination of the most stable phase. This has been done including ZP vibrational energies within the harmonic approximation for a range of pressures and at T=0 K, giving rise to a series of structures that are stable in their respective pressure ranges [8]. Very recently, a treatment of the phases of hydrogen that includes anharmonicity in ZP energies has suggested that relative stability of the phases may change with respect to the calculations within the harmonic approximation [9]. Many of the proposed structures for solid hydrogen have been investigated. Particularly, the Cmca-4 structure, which was found to be the stable one from 385-490 GPa [8], is metallic. Calculations for this structure, within the harmonic approximation for the ionic motion, predict a Tc up to 242 K at 450 GPa [10]. Nonetheless, due to the big ionic displacements, the harmonic approximation may not su ce to describe correctly the system. The aim of this work is to apply a recently developed method to treat anharmonicity, the stochastic self-consistent harmonic approximation (SSCHA) [11], to Cmca-4 metallic hydrogen. This way, we will be able to study the e ects of anharmonicity in the phonon spectrum and to try to understand the changes it may provoque in the value of Tc. The work is structured as follows. First we present the theoretical basis of the calculations: Density Functional Theory (DFT) for the electronic calculations, phonons in the harmonic approximation and the SSCHA. Then we apply these methods to Cmca-4 hydrogen and we discuss the results obtained. In the last chapter we draw some conclusions and propose possible future work.

Stochastic optimal control

H. J. Kushner has obtained the differential equation satisfied by the optimal feedback control law for a stochastic control system in which the plant dynamics and observations are perturbed by independent additive Gaussian white noise processes. However, the differentiation includes the first and second functional derivatives and, except for a restricted set of systems, is too complex to solve with present techniques.

This investigation studies the optimal control law for the open loop system and incorporates it in a sub-optimal feedback control law. This suboptimal control law's performance is at least as good as that of the optimal control function and satisfies a differential equation involving only the first functional derivative. The solution of this equation is equivalent to solving two two-point boundary valued integro-partial differential equations. An approximate solution has advantages over the conventional approximate solution of Kushner's equation.

As a result of this study, well known results of deterministic optimal control are deduced from the analysis of optimal open loop control.

Anisotropic-source-induced changes of the degree of polarization of stochastic electromagnetic beams on propagation

It is shown that stochastic electromagnetic beams may have different degrees of polarization on propagation, even though they have the same coherence properties in the source plane. This fact is due to a possible difference in the anisotropy of the field in the source plane. The result is illustrated by some examples.