<div>We consider actions of the current Lie algebras $\gl_{n}[t]$ and $\gl_{k}[t]$ on the space $\mathfrak{P}_{kn}$ of polynomials in $kn$ anticommuting variables. The actions depend on parameters $\bar{z}=(z_{1}\lc z_{k})$ and $\bar{\alpha}=(\alpha_{1}\lc\alpha_{n})$, respectively.</div><div>We show that the images of the Bethe algebras $\mathcal{B}_{\bar{\alpha}}^{\langle n \rangle}\subset U(\gl_{n}[t])$ and $\mathcal{B}_{\bar{z}}^{\langle k \rangle}\subset U(\gl_{k}[t])$ under these actions coincide.</div><div></div><div>To prove the statement, we use the Bethe ansatz description of eigenvectors of the Bethe algebras via spaces of quasi-exponentials. We establish an explicit correspondence between the spaces of quasi-exponentials describing eigenvectors of $\mathcal{B}_{\bar{\alpha}}^{\langle n \rangle}$ and the spaces of quasi-exponentials describing eigenvectors of $\mathcal{B}_{\bar{z}}^{\langle k \rangle}$.</div><div></div><div>One particular aspect of the duality of the Bethe algebras is that the Gaudin Hamiltonians exchange with the Dynamical Hamiltonians. We study a similar relation between the trigonometric Gaudin and Dynamical Hamiltonians. In trigonometric Gaudin model, spaces of quasi-exponentials are replaced by spaces of quasi-polynomials. We establish an explicit correspondence between the spaces of quasi-polynomials describing eigenvectors of the trigonometric Gaudin Hamiltonians and the spaces of quasi-exponentials describing eigenvectors of the trigonometric Dynamical Hamiltonians.</div><div></div><div>We also establish the $(\gl_{k},\gl_{n})$-duality for the rational, trigonometric and difference versions of Knizhnik-Zamolodchikov and Dynamical equations.</div>

We study a multivariate version of the Bernstein–Sato polynomial, the so-called Bernstein–Sato ideal, associated to an arbitrary factorization of an analytic germ <i>f - f</i>₁···<i>f</i>_r. We identify a large class of geometrically characterized germs so that the <i>D</i>_X,x[<i>s</i>₁,...,<i>s</i>_r]-annihilator of <i>f</i>^s₁¹···<i>f</i>^s_r^r admits the simplest possible description and, more-over, has a particularly nice associated graded object. As a consequence we are able to verify Budur’s Topological Multivariable Strong Monodromy Conjecture for arbitrary factorizations of tame hyperplane arrangements by showing the zero locus of the associated Bernstein–Sato ideal contains a special hyperplane. By developing ideas of Maisonobe and Narvaez-Macarro, we are able to find many more hyperplanes contained in the zero locus of this Bernstein–Sato ideal. As an example, for reduced, tame hyperplane arrangements we prove the roots of the Bernstein–Sato polynomial contained in [−1,0) are combinatorially determined; for reduced, free hyperplane arrangements we prove the roots of the Bernstein–Sato polynomial are all combinatorially determined. Finally, outside the hyperplane arrangement setting, we prove many results about a certain <i>D</i>_X,x-map ∇_<i>A</i> that is expected to characterize the roots of the Bernstein–Sato ideal.

<div>This dissertation focuses on a consistent and conservative Phase-Field method for multiphase flow problems, and it includes both model and scheme development. The first general question addressed in the present study is the multiphase volume distribution problem. A consistent and conservative volume distribution algorithm is developed to solve the problem, which eliminates the production of local voids, overfilling, or fictitious phases, but follows the mass conservation of each phase. One of its applications is to determine the Lagrange multipliers that enforce the mass conservation in the Phase-Field equation, and a reduction consistent conservative Allen-Cahn Phase-Field equation is developed. Another application is to remedy the mass change due to implementing the contact angle boundary condition in the Phase-Field equations whose highest spatial derivatives are second-order. As a result, using a 2nd-order Phase-Field equation to study moving contact line problems becomes possible.</div><div><br></div><div>The second general question addressed in the present study is the coupling between a given physically admissible Phase-Field equation to the hydrodynamics. To answer this general question, the present study proposes the <i>consistency of mass conservation</i> and the <i>consistency of mass and momentum transport</i>, and they are first implemented to the Phase-Field equation written in a conservative form. The momentum equation resulting from these two consistency conditions is Galilean invariant and compatible with the kinetic energy conservation, regardless of the details of the Phase-Field equation. It is further illustrated that the 2nd law of thermodynamics and <i>consistency of reduction</i> of the entire multiphase system only rely on the properties of the Phase-Field equation. All the consistency conditions are physically supported by the control volume analysis and mixture theory. If the Phase-Field equation has terms that are not in a conservative form, those terms are treated by the proposed consistent formulation. As a result, the proposed consistency conditions can always be implemented. This is critical for large-density-ratio problems.</div><div><br></div><div>The consistent and conservative numerical framework is developed to preserve the physical properties of the multiphase model. Several new techniques are developed, including the gradient-based phase selection procedure, the momentum conservative method for the surface force, the boundedness mapping resulting from the volume distribution algorithm, the "DGT" operator for the viscous force, and the correspondences of numerical operators in the discrete Phase-Field and momentum equations. With these novel techniques, numerical analyses ensure that the mass of each phase and momentum of the multiphase mixture are conserved, the order parameters are bounded in their physical interval, the summation of the volume fractions of the phases is unity, and all the consistency conditions are satisfied, on the fully discrete level and for an arbitrary number of phases. Violation of the consistency conditions results in inconsistent errors proportional to the density contrasts of the phases. All the numerical analyses are carefully validated, and various challenging multiphase flows are simulated. The results are in good agreement with the exact/asymptotic solutions and with the existing numerical/experimental data.</div><div> </div><div><br></div><div>The multiphase flow problems are extended to including mass (or heat) transfer in moving phases and solidification/melting driven by inhomogeneous temperature. These are accomplished by implementing an additional consistency condition, i.e., <i>consistency of volume fraction conservation</i>, and the diffuse domain approach. Various problems are solved robustly and accurately despite the wide range of material properties in those problems.</div>

<p> The Bias-Variance Trade-off, where restricting the size of a hypothesis class can limit the generalization error of a model, is a canonical problem in Machine Learning, and a particular issue for high-variance models like Neural Networks that do not have enough parameters to enter the interpolating regime. Regularization techniques add bias to a model to lower testing error at the cost of increasing training error. This paper applies quantum circuits as activation functions in order to regularize a Feed-Forward Neural Network. The network using Quantum Activation Functions is compared against a network of the same dimensions except using Rectified Linear Unit (ReLU) activation functions, which can fit any arbitrary function. The Quantum Activation Function network is then shown to have comparable training performance to ReLU networks, both with and without regularization, for the tasks of binary classification, polynomial regression, and regression on a multicollinear dataset, which is a dataset whose design matrix is rank-deficient. The Quantum Activation Function network is shown to achieve regularization comparable to networks with L2-Regularization, the most commonly used method for neural network regularization today, with regularization parameters in the range of λ ∈ [.1, .5], while still allowing the model to maintain enough variance to achieve low training error. While there are limitations to the current physical implementation of quantum computers, there is potential for future architecture, or hardware-based, regularization methods that leverage the aspects of quantum circuits that provide lower generalization error. </p>

<p>This thesis consists of the structure-preserving numerical methods for PNP-NS equation and dynamic liquid crystal systems in Oseen-Frank energy. </p> <p>In Chapter 1, we give a brief introduction of the Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system, and the dynamical liquid system in Oseen-Frank energy in one-constant approximation case and a special non-one-constant case. Each of those systems has a special structure and properties we want to keep at the discrete level when designing numerical methods.</p> <p>In Chapter 2, we introduce a first-order numerical scheme for the PNP-NS system that is decoupled, positivity preserving, mass conserving, and unconditionally energy stable. The numerical scheme is designed in the context of Wasserstein gradient flow based on the form ∇ · (c∇ ln c). The mobility terms are treated explicitly, and the chemical potential terms are treated implicitly so that the solution of the numerical scheme is the minimizer of a convex functional, which is the key to the unique solvability and positivity preserving of the numer-<br> ical scheme. Proper boundary conditions for chemical potentials are chosen to guarantee the mass-conservation property. The convection term in Poisson-Nernst-Planck(PNP) equation part is treated explicitly with an O(∆t) term introduced so that the numerical scheme is decoupled and unconditionally energy stable. Pressure correction methods are used for the Navier-Stokes(NS) equation part. And we proved the optimal convergence rate with an irregular high-order asymptotic expansion technique.</p> <p>In Chapter 3, we propose a first-order implicit numerical method for a dynamic liquid crystal system in a one-constant-approximation case(which is also known as heat flow of harmonic maps to S2). The solution is the minimizer of a convex functional under the unit length constraint, and from this point, the weak convergence of the numerical scheme could be proved. The numerical scheme is solved in an iterative procedure. This procedure could be proved to be energy decreasing and this implies the convergence of the algorithm.</p> <p>In Chapter 4, we study the dynamic liquid crystal system in a more generalized Oseen- Frank energy compared to Chapter 3. We are assuming K2 = K3 = −K4, the domain Ω is a rectangular region in R3, and d satisfies the periodic boundary condition on ∂Ω. And we propose a class of numerical schemes for this system that preserve the unit length constraint. The convergence of the numerical scheme has been proved under necessary assumptions. And numerical experiments are presented to validate the accuracy and demonstrate the performance of the proposed numerical scheme.</p>

<p dir="ltr">In this thesis, we consider the inverse problem of reconstructing the shape, position, and size of an unknown scattering object. We will talk about different methods used for nondestructive testing in scattering theory. We will consider qualitative reconstruction methods to understand and determine important information about the support of unknown scattering objects. We will also discuss the material properties of the system and connect them to certain crucial aspects of the region of interest, as well as develop useful techniques to determine physical information using inverse scattering theory. </p><p><br></p><p dir="ltr">In the first part of the analysis, we consider the transmission eigenvalue (TE) problem associated with the scattering of a plane wave for an isotropic scatterer. In particular, we examine the transmission eigenvalue problem with two conductivity boundary parameters. In previous studies, this eigenvalue problem was analyzed with one conductive boundary parameter, whereas we will consider the case of two parameters. We will prove the existence and discreteness of the transmission eigenvalues. In addition, we will study the dependence of the TE's on the physical parameters and connect the first transmission eigenvalue to the physical parameters of the problem by a monotone-type argument. Lastly, we will consider the limiting procedure as the second boundary parameter vanishes at the boundary of the scattering region and provide numerical examples to validate the theory presented in Chapter 2. </p><p><br></p><p dir="ltr">The connection between transmission eigenvalues and the system's physical parameters provides a way to do testing in a nondestructive way. However, to understand the region of interest in terms of its shape, size, and position, one needs to use different techniques. As a result, we consider reconstructing extended scatterers using an analogous method to the Direct Sampling Method (DSM), a new sampling method based on the Landweber iteration. We will need a factorization of the far-field operator to analyze the corresponding imaging function for the new Landweber direct sampling method. Then, we use the factorization and the Funk--Hecke integral identity to prove that the new imaging function will accurately recover the scatterer. The method studied here falls under the category of qualitative reconstruction methods, where an imaging function is used to retrieve the scatterer. We prove the stability of our new imaging function as well as derive a discrepancy principle for recovering the regularization parameter. The theoretical results are verified with numerical examples to show how the reconstruction performs by the new Landweber direct sampling method.</p><p><br></p><p dir="ltr">Motivated by the work done with the transmission eigenvalue problem with two conductivity parameters, we also study the direct and inverse problem for isotropic scatterers with two conductive boundary conditions. In such a problem, one analyzes the behavior of the scattered field as one of the conductivity parameters vanishes at the boundary. Consequently, we prove the convergence of the scattered field dealing with two conductivity parameters to the scattered field dealing with only one conductivity parameter. We consider the uniqueness of recovering the coefficients from the known far-field data at a fixed incident direction for multiple frequencies. Then, we consider the inverse shape problem for recovering the scatterer for the measured far-field data at a fixed frequency. To this end, we study the direct sampling method for recovering the scatterer by studying the factorization for the far-field operator. The direct sampling method is stable concerning noisy data and valid in two dimensions for partial aperture data. The theoretical results are verified with numerical examples to analyze the performance using the direct sampling method. </p>

<div><div><div><p>The process of acquiring the large-scale complex systems is usually characterized with cost and schedule overruns. To investigate the causes of this problem, we may view the acquisition of a complex system in several different time scales. At finer time scales, one may study different stages of the acquisition process from the intricate details of the entire systems engineering process to communication between design teams to how individual designers solve problems. At the largest time scale one may consider the acquisition process as series of actions which are, request for bids, bidding and auctioning, contracting, and finally building and deploying the system, without resolving the fine details that occur within each step. In this work, we study the acquisition processes in multiple scales. First, we develop a game-theoretic model for engineering of the systems in the building and deploying stage. We model the interactions among the systems and subsystem engineers as a principal-agent problem. We develop a one-shot shallow systems engineering process and obtain the optimum transfer functions that best incentivize the subsystem engineers to maximize the expected system-level utility. The core of the principal-agent model is the quality function which maps the effort of the agent to the performance (quality) of the system. Therefore, we build the stochastic quality function by modeling the design process as a sequential decision-making problem. Second, we develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. We cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a con- tract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. We study how different parameters in the acquisition procedure affect the bidders’ behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior.</p></div></div></div>

<p> There is a current interest in exploring novel microstructural architectures that take advantage of the response of independent phases. Current guidelines in materials design are not just based on changing the properties of the different phases but also on modifying its base architecture. Hence, the mechanical behavior of composite materials can be adjusted by designing microstructures that alternate stiff and flexible constituents, combined with well-designed architectures. One source of inspiration to achieve these designs is Nature, where biologically mineralized composites can be taken as an example for the design of next-generation structural materials due to their low density, high-strength, and toughness currently unmatched by engineering technologies.</p><p><br></p> <p>The present work focuses on the modeling of biologically inspired composites, where the source of inspiration is the dactyl club of the Stomatopod. Particularly, we built computational models for different regions of the dactyl club, namely: periodic and impact regions. Thus, this research aimed to analyze the effect of microstructure present in the impact and periodic regions in the impact resistance associated with the materials present in the appendage of stomatopods. The main contributions of this work are twofold. First, we built a model that helped to study wave propagation in the periodic region. This helped to identify possible bandgaps and their influence on the wave propagation through the material. Later on, we extended what we learned from this material to study the bandgap tuning in bioinspired composites. Second, we helped to unveil new microstructural features in the impact region of the dactyl club. Specifically, the sinusoidally helicoidal composite and bicontinuous particulate layer. For these, structural features we developed finite element models to understand their mechanical behavior.</p><p><br></p> <p>The results in this work help to elucidate some new microstructures and present some guidelines in the design of architectured materials. By combining the current synthesis and advanced manufacturing methods with design elements from these biological structures we can realize potential blueprints for a new generation of advanced materials with a broad range of applications. Some of the possible applications include impact- and vibration-resistant coatings for buildings, body armors, aircraft, and automobiles, as well as in abrasion- and impact-resistant wind turbines.</p><br>

<p dir="ltr">In Chapter 1, we introduce geometric motions from the general perspective of gradient flows. Here we develop the basic framework in which to pose the two main results of this thesis.</p><p dir="ltr">In Chapter 2, we examine the pinch-off phenomenon for a tubular surface moving by surface diffusion. We prove the existence of a one parameter family of pinching profiles obeying a long wavelength approximation of the dynamics.</p><p dir="ltr">In Chapter 3, we study a diffusion-based numerical scheme for curve shortening flow. We prove that the scheme is one time-step consistent.</p>