This thesis concerns how to compute upper and lower bounds for the eigenvalues of self-adjoint operators. We discuss two different methods: the so-called quadratic method and the Zimmermann-Mertins method. We know that the classical methods of computing the spectrum of a self-adjoint operator often lead to spurious eigenvalues in gaps between two parts of the essential spectrum. The methods to be examined have been studied recently in connection with the phenomenon of spectral pollution. In the first part of the thesis we show how to obtain enclosures of the eigenvalues in both the quadratic method and the Zimmermann-Mertins method. We examine the convergence properties of these methods for computing corresponding upper and lower bounds in the case of semi-definite self-adjoint operators with compact resolvent. In the second part of the thesis we find concrete asymptotic bounds for the size of the enclosure and study their optimality in the context of one-dimensional Schr¨odinger operators. The effectiveness of these methods is then illustrated by numerical experiments on the harmonic and the anharmonic oscillators. We compare these two methods, and establish which one is better suited in terms of accuracy and efficiency.

In this thesis we study travelling wave solutions and self-similar solutions for a green roof model and for some simpler models which are derived from that model. We focus on two limiting cases near a dry region and near a saturated region. We start by considering a convection model in the absence of diffusion and sink terms. We show that rarefaction waves and shock solutions exist for several cases. Next, we consider a convection-diffusion model where both the convective and diffusive terms are present and we show that travelling wave solutions and self-similar solutions exist for some cases. Moreover, numerical simulations are used for the travelling wave and self-similar solutions and confirm the analytic predictions. Finally, we consider the green roof model where all terms are present and we show that travelling wave solutions exist, whereas self-similar solutions are not found. We also show the travelling wave solutions exist for the two limiting cases.

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This thesis is concerned with the automation of solving optimisation problems constrained by partial differential equations (PDEs). Gradient-based optimisation algorithms are the key to solve optimisation problems of practical interest. The required derivatives can be efficiently computed with the adjoint approach. However, current methods for the development of adjoint models often require a significant amount of effort and expertise, in particular for non-linear time-dependent problems. This work presents a new high-level reinterpretation of algorithmic differentiation to develop adjoint models. This reinterpretation considers the discrete system as a sequence of equation solves. Applying this approach to a general finite-element framework results in an automatic and robust way of deriving and solving adjoint models. This drastically reduces the development effort compared to traditional methods. Based on this result, a new framework for rapidly defining and solving optimisation problems constrained by PDEs is developed. The user specifies the discrete optimisation problem in a compact high-level language that resembles the mathematical structure of the underlying system. All remaining steps, including parameter updates, PDE solves and derivative computations, are performed without user intervention. The framework can be applied to a wide range of governing PDEs, and interfaces to various gradient-free and gradient-based optimisation algorithms. The capabilities of this framework are demonstrated through the application to two PDE-constrained optimisation problems. The first is concerned with the optimal layout of turbines in tidal stream farms; this optimisation problem is one of the main challenges facing the marine renewable energy industry. The second application applies data assimilation to reconstruct the profile of tsunami waves based on inundation observations. This provides the first step towards the general reconstruction of tsunami signals from satellite information.

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