[pt] Esta dissertação estuda o comportamento dinâmico não linear de colunas de perfuração de poços de petróleo. A coluna de perfuração é uma estrutura longa, flexível e esbelta, responsável pela perfuração propriamente dita. Seus elementos e funções são apresentados e uma análise numérica é realizada posteriormente. Foi desenvolvido um programa utilizando o software MATLAB (marca registrada) para simulação numérica do comportamento dinâmico das colunas pelo método dos elementos finitos que utiliza a formulação corotacional para implementação da não linearidade geométrica. A discretização da estrutura utiliza um elemento de viga com seis graus de liberdade por nó aplicando a formulação de viga de Euler-Bernoulli. Para solução do sistema de equações não lineares resultante utiliza-se o método de Newton-Raphson. Além disso, o método de Newmark é utilizado para integração no tempo das equações de movimento do problema. Um modelo com molas lineares é proposto para representar o contato entre a parede do poço e a coluna. A metodologia proposta e as funcionalidades do programa desenvolvido são avaliadas e seus resultados são comparados com algumas soluções analíticas ou numéricas de exemplos disponíveis na literatura. Esses resultados conferem confiabilidade na análise de problemas de coluna de perfuração, que apresentam as séries temporais de deslocamentos e esforços em toda a coluna e os modos de flambagem gerados. Os resultados obtidos demonstram que a coluna é muito sensível a qualquer mudança de condição de contorno, o que corrobora com a complexidade do problema. Assim, o trabalho fornece uma base razoável para desenvolvimentos posteriores, que permitam a análise de toda a coluna de perfuração acoplada. / [en] This work studies the nonlinear dynamic behavior of oil well drillstring, which is a long slender flexible structure responsible for the drilling. Its elements and functions are presented, and numerical analyses are performed later. The work develops a computational code using the software MATLAB (trademark) for the numerical simulation of the column s dynamic behavior using the finite element method. The corotational formulation is used for the implementation of geometric nonlinearity. The structure s discretization uses a beam element with six degrees of freedom per node and employs the Euler-Bernoulli s beam formulation. The Newton-Raphson method is responsible for solving the nonlinear system of equations. In addition, the solution procedure uses the Newmark s method for the time integration of the problem s movement equations. A linear setup spring model is proposed to represent the contact between the borehole wall and the column. The proposed methodology and computational code capabilities are evaluated by comparing some results to analytical or numerical results of examples available in the literature. These results give reliability to analyze drillstring problems, which present the displacements and forces time series of the whole column and the buckling modes generated. The results show that the column is very sensitive to any boundary condition changing, which corroborates the complexity of the problem. Hence, the work proposes a reasonable basis for further developments, allowing the entire coupled drillstring analysis.

<div>On rectangular meshes, the simplest spectral element method for elliptic equations is the classical Lagrangian <i>Q</i>^k finite element method with only (<i>k</i>+1)-point Gauss-Lobatto quadrature, which can also be regarded as a finite difference scheme on all Gauss-Lobatto points. We prove that this finite difference scheme is (<i>k</i> + 2)-th order accurate for <i>k</i> ≥ 2, whereas <i>Q</i>^<i>k</i> spectral element method is usually considered as a (<i>k</i> + 1)-th order accurate scheme in <i>L²</i>-norm. This result can be extended to linear wave, parabolic and linear Schrödinger equations.</div><div><br></div><div><div>Additionally, the <i>Q^k</i> finite element method for elliptic problems can also be viewed as a finite difference scheme on all Gauss-Lobatto points if the variable coefficients are replaced by their piecewise <i>Q^k </i>Lagrange interpolants at the Gauss Lobatto points in each rectangular cell, which is also proven to be (<i>k</i> + 2)-th order accurate.</div></div><div><br></div><div><div>Moreover, the monotonicity and discrete maximum principle can be proven for the fourth order accurate Q2 scheme for solving a variable coefficient Poisson equation, which is the first monotone and high order accurate scheme for a variable coefficient elliptic operator.</div></div><div><br></div><div><div>Last but not the least, we proved that certain high order accurate compact finite difference methods for convection diffusion problems satisfy weak monotonicity. Then a simple limiter can be designed to enforce the bound-preserving property when solving convection diffusion equations without losing conservation and high order accuracy.</div><div><br></div></div>

In this thesis, we first propose a new scalar auxiliary variable (SAV) approach for general dissipative nonlinear systems. This new approach is half computational cost of the original SAV approach, can be extended to high order unconditionally energy stable backward differentiation formula (BDF) schemes and not restricted to the gradient flow structure. Rigorous error estimates for this new SAV approach are conducted for the Allen-Cahn and Cahn-Hilliard type equations from the BDF1 to the BDF5 schemes in a unified form. As an application of this new approach, we construct high order unconditionally stable, fully discrete schemes for the incompressible Navier-Stokes equation with periodic boundary condition. The corresponding error estimates for the fully discrete schemes are also reported. Secondly, by combining the new SAV approach with functional transformation, we propose a new method to construct high-order, linear, positivity/bound preserving and unconditionally energy stable schemes for general dissipative systems whose solutions are positivity/bound preserving. We apply this new method to second order equations: the Allen-Cahn equation with logarithm potential, the Poisson-Nernst-Planck equation and the Keller-Segel equations and fourth order equations: the thin film equation and the Cahn-Hilliard equation with logarithm potential. Ample numerical examples are provided to demonstrate the improved efficiency and accuracy of the proposed method.

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<p dir="ltr">This work introduces an innovative algorithm for co-simulating time-dependent Drift Diffusion (DD) equations with Maxwell\textquotesingle s equations to characterize semiconductor devices. Traditionally, the DD equations, derived from the Boltzmann transport equations, are used alongside Poisson\textquotesingle s equation to model electronic carriers in semiconductors. While DD equations coupled with Poisson\textquotesingle s equation underpin commercial TCAD software for micron-scale device simulation, they are limited by electrostatic assumptions and fail to capture time dependent high-frequency effects. Maxwell\textquotesingle s equations are fundamental to classical electrodynamics, enabling the prediction of electrical performance across frequency range crucial to advanced device fabrication and design. However, their integration with DD equations has not been studied thoroughly. The proposed method advances current simulation techniques by introducing a new broadband patch-based method to solve time-domain 3-D Maxwell\textquotesingle s equations and integrating it with the solution of DD equations. This technique is free of the low-frequency breakdown issues prevalent in conventional full-wave simulations. Meanwhile, it enables large-scale simulations with reduced computational complexity. This work extends the simulation to encompass the complete device, including metal contacts and interconnects. Thus, it captures the entire electromagnetic behavior, which is especially critical in electrically larger systems and high-frequency scenarios. The electromagnetic interactions of the device with its contacts and interconnects are investigated, providing insights into performance at the chip level. Validation through numerical experiments and comparison with results from commercial TCAD tools confirm the effectiveness of the proposed method. </p>

<p dir="ltr">Matrix decompositions play a pivotal role in matrix computation and applications. While general dense matrix-vector multiplications and linear equation solvers are prohibitively expensive, matrix decompositions offer fast alternatives for matrices meeting specific properties. This dissertation delves into my contributions to two fast matrix multiplication algorithms and one fast linear equation solver algorithm tailored for certain matrices and applications, all based on efficient matrix decompositions. Fast dimensionality reduction methods in spectral clustering, based on efficient eigen-decompositions, are also explored.</p><p dir="ltr">The first matrix decomposition introduced is the "kernel-independent" interpolative decomposition butterfly factorization (IDBF), acting as a data-sparse approximation for matrices adhering to a complementary low-rank property. Constructible in $O(N\log N)$ operations for an $N \times N$ matrix via hierarchical interpolative decompositions (IDs), the IDBF results in a product of $O(\log N)$ sparse matrices, each with $O(N)$ non-zero entries. This factorization facilitates rapid matrix-vector multiplication in $O(N \log N)$ operations, making it a versatile framework applicable to various scenarios like special function transformation, Fourier integral operators, and high-frequency wave computation.</p><p dir="ltr">The second matrix decomposition accelerates matrix-vector multiplication for computing multi-dimensional Jacobi polynomial transforms. Leveraging the observation that solutions to Jacobi's differential equation can be represented through non-oscillatory phase and amplitude functions, the corresponding matrix is expressed as the Hadamard product of a numerically low-rank matrix and a multi-dimensional discrete Fourier transform (DFT) matrix. This approach utilizes $r^d$ fast Fourier transforms (FFTs), where $r = O(\log n / \log \log n)$ and $d$ is the dimension, resulting in an almost optimal algorithm for computing the multidimensional Jacobi polynomial transform.</p><p dir="ltr">An efficient numerical method is developed based on a matrix decomposition, Hierarchical Interpolative Factorization, for solving modified Poisson-Boltzmann (MPB) equations. Addressing the computational bottleneck of evaluating Green's function in the MPB solver, the proposed method achieves linear scaling by combining selected inversion and hierarchical interpolative factorization. This innovation significantly reduces the computational cost associated with solving MPB equations, particularly in the evaluation of Green's function.</p><p dir="ltr"><br></p><p dir="ltr">Finally, eigen-decomposition methods, including the block Chebyshev-Davidson method and Orthogonalization-Free methods, are proposed for dimensionality reduction in spectral clustering. By leveraging well-known spectrum bounds of a Laplacian matrix, the Chebyshev-Davidson methods allow dimensionality reduction without the need for spectrum bounds estimation. And instead of the vanilla Chebyshev-Davidson method, it is better to use the block Chebyshev-Davidson method with an inner-outer restart technique to reduce total CPU time and a progressive polynomial filter to take advantage of suitable initial vectors when available, for example, in the streaming graph scenario. Theoretically, the Orthogonalization-Free method constructs a unitary isomorphic space to the eigenspace or a space weighting the eigenspace, solving optimization problems through Gradient Descent with Momentum Acceleration based on Conjugate Gradient and Line Search for optimal step sizes. Numerical results indicate that the eigenspace and the weighted eigenspace are equivalent in clustering performance, and scalable parallel versions of the block Chebyshev-Davidson method and OFM are developed to enhance efficiency in parallel computing.</p>

<p dir="ltr">This thesis delves into the development and integration of energy-dissipative methods, with applications spanning numerical analysis, optimization, and deep neural networks, primarily targeting gradient flows and porous medium equations. In the realm of optimization, we introduce the element-wise relaxed scalar auxiliary variable (E-RSAV) algorithm, showcasing its robustness and convergence through extensive numerical experiments. Complementing this, we design an Energy-Dissipative Evolutionary Deep Operator Neural Network (DeepONet) to numerically address a suite of partial differential equations. By employing a dual-subnetwork structure and utilizing the Scalar Auxiliary Variable (SAV) method, the network achieves impeccable approximations of operators while upholding the Energy Dissipation Law, even when training data comprises only the initial state. Lastly, we formulate first-order schemes tailored for Wasserstein gradient flows. Our schemes demonstrate remarkable properties, including mass conservation, unique solvability, positivity preservation, and unconditional energy dissipation. Collectively, the innovations presented here offer promising pathways for efficient and accurate numerical solutions in both gradient flows and Wasserstein gradient flows, bridging the gap between traditional optimization techniques and modern neural network methodologies.</p>

Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable regions. Thus, it is of great interest to implement an appropriate optimisation approach, which can preserve the computational resources and come up with a global optimum. While not being trapped in local optima, as well as the feasibility to overcome some other matters such as nonlinear and phenomena of discontinuous with a large number of variables. Problems such as lengthy computation time, constraints put forward for antenna requirements and demand for large computer memory, are very common in the analysis due to the increased interests in tackling high-scale, more complex and higher-dimensional problems. On the other side, demands for even more accurate results always expand constantly. In the context of this statement, it is very important to find out how the recently developed optimization roles can contribute to the solution of the aforementioned problems. Thereafter, the key goals of this work are to model, study and design low profile antennas for wireless and mobile communications applications using optimization process over a computational electromagnetics numerical solution. The numerical solution method could be performed over one or hybrid methods subjective to the design antenna requirements and its environment. Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra- Wideband antenna. The fitness functions and the geometrical antenna elements required for such design are considered. Two antennas are designed, implemented and measured. The computed and measured outcomes are found in reasonable agreement. Secondly, the work is also addressed on how the resonance modes of microstrip patches could be performed using the method of Moments. Results have been shown on how the modes could be adjusted using MoM. Finally, the design implications of balanced structure for mobile handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through using firefly algorithm method. The optimised balanced antenna exhibits reasonable matching performance including near-omnidirectional radiations over the dual desirable operating bands with reduced EMF, which leads to a great immunity improvement towards the hand-held. / General Secretariat of Education and Scientific Research Libya

<p dir="ltr">This thesis presents advances in numerical methods for partial differential equations (PDEs) and optimization problems, with a focus on improving efficiency, stability, and accuracy across various applications. We begin by addressing 3D Poisson-type equations, developing a GPU-accelerated spectral-element method that utilizes the tensor product structure to achieve extremely fast performance. This approach enables solving problems with over one billion degrees of freedom in less than one second on modern GPUs, with applications to Schrödinger and Cahn<i>–</i>Hilliard equations demonstrated. Next, we focus on parabolic PDEs, specifically the Cahn<i>–</i>Hilliard equation with dynamical boundary conditions. We propose an efficient energy-stable numerical scheme using a unified framework to handle both Allen<i>–</i>Cahn and Cahn<i>–</i>Hilliard type boundary conditions. The scheme employs a scalar auxiliary variable (SAV) approach to achieve linear, second-order, and unconditionally energy stable properties. Shifting to a machine learning perspective for PDEs, we introduce an unsupervised learning-based numerical method for solving elliptic PDEs. This approach uses deep neural networks to approximate PDE solutions and employs least-squares functionals as loss functions, with a focus on first-order system least-squares formulations. In the realm of optimization, we present an efficient and robust SAV based algorithm for discrete gradient systems. This method modifies the standard SAV approach and incorporates relaxation and adaptive strategies to achieve fast convergence for minimization problems while maintaining unconditional energy stability. Finally, we address optimization in the context of machine learning by developing a structure-guided Gauss<i>–</i>Newton method for shallow ReLU neural network optimization. This approach exploits both the least-squares and neural network structures to create an efficient iterative solver, demonstrating superior performance on challenging function approximation problems. Throughout the thesis, we provide theoretical analysis, efficient numerical implementations, and extensive computational experiments to validate the proposed methods. </p>

The main motivation behind writing this thesis was to construct numerical methods to approximate solutions to differential equations driven by rough paths, where the solution is considered in the rough path-sense. Rough paths of inhomogeneous degree of smoothness as driving noise are considered. We also aimed to find applications of these numerical methods to stochastic differential equations. After sketching the core ideas of the Rough Paths Theory in Chapter 1, the versions of the core theorems corresponding to the inhomogeneous degree of smoothness case are stated and proved in Chapter 2 along with some auxiliary claims on the continuity of the solution in a certain sense, including an RDE-version of Gronwall's lemma. In Chapter 3, numerical schemes for approximating solutions to differential equations driven by rough paths of inhomogeneous degree of smoothness are constructed. We start with setting up some principles of approximations. Then a general class of local approximations is introduced. This class is used to construct global approximations by pasting together the local ones. A general sufficient condition on the local approximations implying global convergence is given and proved. The next step is to construct particular local approximations in finite dimensions based on solutions to ordinary differential equations derived locally and satisfying the sufficient condition for global convergence. These local approximations require strong conditions on the one-form defining the rough differential equation. Finally, we show that when the local ODE-based schemes are applied in combination with rough polynomial approximations, the conditions on the one-form can be weakened. In Chapter 4, the results of Gyurko & Lyons (2010) on path-wise approximation of solutions to stochastic differential equations are recalled and extended to the truncated signature level of the solution. Furthermore, some practical considerations related to the implementation of high order schemes are described. The effectiveness of the derived schemes is demonstrated on numerical examples. In Chapter 5, the background theory of the Kusuoka-Lyons-Victoir (KLV) family of weak approximations is recalled and linked to the results of Chapter 4. We highlight how the different versions of the KLV family are related. Finally, a numerical evaluation of the autonomous ODE-based versions of the family is carried out, focusing on SDEs in dimensions up to 4, using cubature formulas of different degrees and several high order numerical ODE solvers. We demonstrate the effectiveness and the occasional non-effectiveness of the numerical approximations in cases when the KLV family is used in its original version and also when used in combination with partial sampling methods (Monte-Carlo, TBBA) and Romberg extrapolation.