Computation of approximate fuel-optimal control

Iterative digital computer determination of optimal fuel control in linear time-invariant plan

Identification of Systems

Quasilinearization for system identification and programming strategie

Regularisation methods for imaging from electrical measurements

In Electrical Impedance Tomography the conductivity of an object is estimated from boundary measurements. An array of electrodes is attached to the surface of the object and current stimuli are applied via these electrodes. The resulting voltages are measured. The process of estimating the conductivity as a function of space inside the object from voltage measurements at the surface is called reconstruction. Mathematically the ElT reconstruction is a non linear inverse problem, the stable solution of which requires regularisation methods. Most common regularisation methods impose that the reconstructed image should be smooth. Such methods confer stability to the reconstruction process, but limit the capability of describing sharp variations in the sought parameter. In this thesis two new methods of regularisation are proposed. The first method, Gallssian anisotropic regularisation, enhances the reconstruction of sharp conductivity changes occurring at the interface between a contrasting object and the background. As such changes are step changes, reconstruction with traditional smoothing regularisation techniques is unsatisfactory. The Gaussian anisotropic filtering works by incorporating prior structural information. The approximate knowledge of the shapes of contrasts allows us to relax the smoothness in the direction normal to the expected boundary. The construction of Gaussian regularisation filters that express such directional properties on the basis of the structural information is discussed, and the results of numerical experiments are analysed. The method gives good results when the actual conductivity distribution is in accordance with the prior information. When the conductivity distribution violates the prior information the method is still capable of properly locating the regions of contrast. The second part of the thesis is concerned with regularisation via the total variation functional. This functional allows the reconstruction of discontinuous parameters. The properties of the functional are briefly introduced, and an application in inverse problems in image denoising is shown. As the functional is non-differentiable, numerical difficulties are encountered in its use. The aim is therefore to propose an efficient numerical implementation for application in ElT. Several well known optimisation methods arc analysed, as possible candidates, by theoretical considerations and by numerical experiments. Such methods are shown to be inefficient. The application of recent optimisation methods called primal- dual interior point methods is analysed be theoretical considerations and by numerical experiments, and an efficient and stable algorithm is developed. Numerical experiments demonstrate the capability of the algorithm in reconstructing sharp conductivity profiles

Geometric Foundations of Gravity and Applications

The thesis is split into three parts: In the first part we describe the Geometric Trinity of Gravity, i.e. the three alternative formulations of gravitational interactions. General Relativity uses the curvature of spacetime to describe gravity. However, there are two other alternative but dynamically equivalent formulations: the Teleparallel theory of gravity, which suggests that gravity is mediated through the torsion of spacetime and the Symmetric Teleparallel gravity that assigns gravity to the non-metricity of spacetime. In addition, we discuss possible modifications in each case. In the second part, we use Lie and Noether symmetries of modified theories of gravity as a geometric criterion to classify them on those that are invariant under point transformations. Furthermore, we calculate the invariants of each symmetry and use them to reduce the dynamics of each system in order to find exact cosmological solutions. However, modified theories should also behave ``correctly'' at astrophysical scales too. That is why, in the last part, we use the notion of the maximum turnaround radius of a structure as a stability criterion to test theories of gravity. Specifically, we derive a general formula for the maximum turnaround radius, which denotes the maximum size that a structure can have, for all theories that respect the Einstein Equivalence Principle. Finally, we apply this formula to the Brans-Dicke and the $f(R)$ theories and discuss the requirements for the stability of large scale structures in their framework

Tensor methods for large sparse systems of nonlinear equations

This paper introduces censor methods for solving, large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium- sized dense problems. They base each iteration on a quadratic model of the nonlinear equations. where the second-order term is selected so that the model requires no more derivative or function information per iteration than standard linear model-based methods, and hardly more storage or arithmetic operations per iteration. Computational experiments on small to medium-sized problems have shown censor methods to be considerably more efficient than standard Newton-based methods, with a particularly large advantage on singular problems. This paper considers the extension of this approach to solve large sparse problems. The key issue that must be considered is how to make efficient use of sparsity in forming and solving the censor model problem at each iteration. Accomplishing this turns out to require an entirely new way of solving the tensor model that successfully exploits the sparsity of the Jacobian, whether the Jacobian is nonsingular or singular. We develop such an approach and, based upon it, an efficient tensor method for solving large sparse systems of nonlinear equations. Test results indicate that this tensor method is significantly more efficient and robust than an efficient sparse Newton-based method. in terms of iterations, function evaluations. and execution time