Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]

This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow

Development of an evolutionary algorithm for crystal structure prediction

Die vorliegende Dissertation befasst sich mit der theoretischen Vorhersage neuer Materialien. Ein evolutionärer Algorithmus, der zur Lösung dieses globalen Optimierungsproblems Konzepte der natürlichen Evolution imitiert, wurde entwickelt und ist als Programmpaket EVO frei verfügbar. EVO findet zuverlässig sowohl bekannte als auch neuartige Kristallstrukturen. Beispielsweise wurden die Strukturen von Germaniumnitrofluorid, einer neue Borschicht und mit dem gekreuzten Graphen einer bisher unbekannte Kohlenstoffstruktur gefunden. Ferner wurde in der Arbeit gezeigt, dass das reine Auffinden solcher Strukturen der erste Teil einer erfolgreichen Vorhersage ist. Weitere aufwendige Berechnungen sind nötig, die Aufschluss über die Stabilität der hypothetischen Struktur geben und Aussagen über zu erwartende Materialeigenschaften liefern

A novel method to rapidly fit conductance-based models to individual neurons

In this thesis, I present a new method of model optimisation that allows the calibration of conductance-based models of neuronal membrane potential to data from just a single neuron, and achieves good correspondence with the reference data in mere minutes. These properties are desirable because they allow investigations of individual variability among neurons of a given type, of homoeostatic processes and non-synaptic plasticity events, as well as of the contribution of particular neuronal properties to the dynamics of small circuits. In the first chapter, the thesis introduces in detail the working principle of the method, which can be summed up as model optimisation using stimuli to isolate parameter subsets (“MOSTIPS”), and represents a major part of the work and novelty of this project. The second chapter focusses on the construction of accurate models of two mammalian potassium channels which, being ectopically expressed in Xenopus laevis oocytes, served as a validation tool for the new method. In the third chapter, I evaluate the new method, presenting results from fitting models to data from synthetic sources as well as the above-mentioned oocytes. Finally, the fourth chapter contains a number of related results from closed-loop electrophysiology approaches, including extensions to the dynamic clamp protocol for both single neurons and hybrid circuits composed of live and simulated neurons, as well as preliminary results from a closed-loop model fitting approach closely related to the main work presented above. The thesis concludes that the newly developed approaches to model fitting constitute valuable additions to existing methods. The MOSTIPS method achieves tightly constrained parametrisations using both less data and less processing time than classical methods, while the related closed-loop fitting approach produces results that closely follow ongoing changes in evoked activity patterns in real time. Conversely, some issues have been left unanswered, including the contribution of the stimulus generation and selection algorithm, the success of which I have been unable to establish, as well as whether the methods developed herein can reliably identify relevant properties of individual cells. Nevertheless, both the particular methods and the general approach of using prior estimates of the model and its parameter values to propose stimulus patterns represent major advances in the field of neuron model optimisation

A Genetic Algorithm for Structure Prediction of Magnetic Materials

When considering global optimisation of magnetic crystal structures, it is important to consider both the atomic and spin degrees of freedom. This thesis presents a novel genetic algorithm for simultaneously optimising the magnetic and crystal structures of materials. The algorithm was first tested on a new magnetic interatomic potential presented in the thesis, and was shown to be capable of finding the correct atomic and magnetic structure. The algorithm was then used to study mixing the NiO(111)/MgO(111) interface, where the process behind the mixing was unknown. Results from this study suggest that mixing is driven by energetics of the system, rather than kinetic processes. Finally, the interface between the Heusler alloy CFAS and n-doped Ge, where experimental observations suggested an unknown interface phase, was studied. This work proposed the half Heusler structure for this phase, and predicted this to have unfavourable electronic properties

Distributed task allocation optimisation techniques in multi-agent systems

A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake real-time environments, such as rescue missions, it is difficult to predict in advance what the requirements of the mission will be, what resources will be available, and how to optimally employ such resources. Yet, a fast response and speedy execution are critical to the outcome. This thesis proposes distributed optimisation techniques to tackle the following questions: how to maximise the number of assigned tasks in time restricted environments with limited resources; how to reach consensus on an execution plan across many agents, within a reasonable time-frame; and how to maintain robustness and optimality when factors change, e.g. the number of agents changes. Three novel approaches are proposed to address each of these questions. A novel algorithm is proposed to reassign tasks and free resources that allow the completion of more tasks. The introduction of a rank-based system for conflict resolution is shown to reduce the time for the agents to reach consensus while maintaining equal number of allocations. Finally, this thesis proposes an adaptive data-driven algorithm to learn optimal strategies from experience in different scenarios, and to enable individual agents to adapt their strategy during execution. A simulated rescue scenario is used to demonstrate the performance of the proposed methods compared with existing baseline methods

Lemon: an MPI parallel I/O library for data encapsulation using LIME

We introduce Lemon, an MPI parallel I/O library that is intended to allow for efficient parallel I/O of both binary and metadata on massively parallel architectures. Motivated by the demands of the Lattice Quantum Chromodynamics community, the data is stored in the SciDAC Lattice QCD Interchange Message Encapsulation format. This format allows for storing large blocks of binary data and corresponding metadata in the same file. Even if designed for LQCD needs, this format might be useful for any application with this type of data profile. The design, implementation and application of Lemon are described. We conclude with presenting the excellent scaling properties of Lemon on state of the art high performance computers