Mathematics and Statisticshttp://hdl.handle.net/10523/962018-05-22T17:52:41Z2018-05-22T17:52:41ZAsymptotics of solutions in evolutionary formulations of the Einstein constraint equations.Ritchie, Joshuahttp://hdl.handle.net/10523/80462018-05-21T14:02:07Z2018-05-20T23:49:26ZAsymptotics of solutions in evolutionary formulations of the Einstein constraint equations.
2018
Ritchie, Joshua
Performing a 2+1 split of an initial data set allows one to formulate the Einstein constraint equations as an initial value problem. There are two possible sets of equations that can arise here, the parabolic-hyperbolic equations and the strictly hyperbolic equations. The fundamental difference between the two formulations is in how the 2+1 lapse is treated. We study the asymptotic behaviour of solutions in both formulations by considering perturbations to known solutions of the constraint equations. The thesis then ends by introducing a model to produce data sets that describe binary black holes in the parabolic-hyperbolic formulations.
2018-05-20T23:49:26ZContemporary wave–ice interaction modelsMosig, Johannes Ernst Manfredhttp://hdl.handle.net/10523/79582018-03-23T13:02:08Z2018-03-23T02:56:15ZContemporary wave–ice interaction models
2018
Mosig, Johannes Ernst Manfred
Sea ice is an important indicator and agent of changes in the global climate system. The ice is affected by waves that travel into the Marginal Ice Zone (MIZ) and cause floes to raft, deform and, potentially, fracture. The resulting change in the floe size distribution (FSD) influences the melting and freezing. Simultaneously, the ice floes affect the propagation of ocean waves. The motivation to study wave--ice interaction is therefore twofold: it plays a role in understanding climate change, and it is vital to wave forecasting models that have to be accurate to ensure the safety of research expeditions, coastal communities, etc. In the present thesis we investigate various models of ocean wave propagation in ice infested seas.
We distinguish between three classes of models: "floe models", "effective medium models", and "transport equation models", each of which assume a different set of fundamental degrees of freedom. Our goal is to systematically explore existing models of each type and extend them to advance our understanding of wave-ice interactions.
Floe models resolve individual ice floes as their fundamental degrees of freedom. We consider the scattering of water waves in a two-dimensional domain from a floating sea ice floe of uncertain length. The length is treated as a random variable governed by a prescribed probability distribution. In accord with the majority of wave-ice interaction models, a thin elastic plate that floats with Archimedean draught is used to represent the ice floe. We compute the expectation and variance of the reflection and transmission coefficients using two different methods derived from the framework of generalized polynomial chaos (gPC), which affords the expansion of unknown quantities of the problem in a basis of orthogonal polynomials of the random variable. The gPC methods are shown to be numerically efficient and exhibit desirable exponential convergence properties, as opposed to the slow algebraic convergence of the quasi Monte Carlo approach that we use for comparison. Finally, we employ one of the gPC methods to demonstrate that the FSD can have a significant impact on the expected transmission coefficient.
Effective medium models describe the surface ocean layer (including ice floes, brash ice, etc.) as a homogeneous viscoelastic material that causes waves to attenuate as they travel through the medium. We compare three ice layer models, namely a viscoelastic fluid layer model currently being used for studies in the spectral wave model WAVEWATCH III and two simpler viscoelastic thin beam models. A comparative analysis shows that one of the beam models provides similar predictions for wave attenuation and wavelength to the viscoelastic fluid model. We also calibrate the three models using wave attenuation data recently collected in the Antarctic MIZ. Although agreement with the data is obtained with all three models, several important issues related to the viscoelastic fluid model are identified that raise questions about its suitability to characterize wave attenuation in ice-covered seas.
Transport equation models describe the propagation of the wave action density (which is proportional to the wave energy density) in terms of a transport equation that is commonly used in ocean wave modelling. A term to represent the effect of floating sea ice is known for sparse collections of floes, but this is not valid at high concentrations. As a result, we derive the transport equation for a continuous ice cover of random thickness as a first step towards a transport equation model for high ice concentration. The attenuation coefficients predicted by this new equation turn out to be unrealistic. Hence, we outline an alternative derivation that may be explored in future work.
2018-03-23T02:56:15ZSemiparametric dispersal kernels in stochastic spatiotemporal epidemic modelsLuo, Peihttp://hdl.handle.net/10523/79392018-03-21T13:02:05Z2018-03-20T23:01:38ZSemiparametric dispersal kernels in stochastic spatiotemporal epidemic models
2018
Luo, Pei
The dispersal kernel plays a fundamental role in stochastic spatiotemporal epidemic models. By quantifying the rate at which an infectious source infects a susceptible individual in terms of their separation distance, the dispersal kernel is able to account for the observed spatial characteristics of an epidemic. The aim of this thesis is to construct a dispersal kernel which belongs to a semiparametric family. We introduce a new concept called the natural bridge basis in order to build the semiparametrized dispersal kernel. We use data from a citrus canker epidemic in Florida to illustrate and examine our approach. We find features of the semiparametrized dispersal kernel which were not previously evident in parametrized dispersal kernels.
2018-03-20T23:01:38ZModelling strategies to improve genetic evaluation for the New Zealand sheep industryHolmes, John Barretthttp://hdl.handle.net/10523/78852018-03-06T13:02:08Z2018-03-05T20:24:56ZModelling strategies to improve genetic evaluation for the New Zealand sheep industry
2018
Holmes, John Barrett
The question of how best to optimise the accuracy of genetic evaluation for livestock populations has been given new life by the advent of genomics. Therefore we will investigate methods of evaluating and/or improving the accuracy of genetic evaluation in ways applicable to genotyped populations, while trying to maximise computational efficiency. We will explore modelling strategies with utility outside animal breeding, including examples of these potential non-animal breeding applications.
2018-03-05T20:24:56ZMass loss due to gravitational waves with a cosmological constantSaw, Vee-Liemhttp://hdl.handle.net/10523/77052018-02-28T01:08:30Z2017-11-07T20:08:00ZMass loss due to gravitational waves with a cosmological constant
2017
Saw, Vee-Liem
The theoretical basis for the energy carried away by gravitational waves that an isolated gravitating system emits was first formulated by Hermann Bondi during the 1960s. Recent findings from looking at distant supernovae reveal that the rate of expansion of our Universe is accelerating, which may be well-explained by including a positive cosmological constant into the Einstein field equations for general relativity. By solving the Newman-Penrose equations (which are equivalent to the Einstein field equations), we generalise this notion of Bondi mass-energy and thereby provide a firm theoretical description of how an isolated gravitating system loses energy as it radiates gravitational waves in a Universe that expands at an accelerated rate.
2017-11-07T20:08:00ZThe structure of GCR and CCR groupoid C*-algebrasvan Wyk, Daniel Willemhttp://hdl.handle.net/10523/75832017-10-10T23:52:08Z2017-10-10T23:50:12ZThe structure of GCR and CCR groupoid C*-algebras
2017
van Wyk, Daniel Willem
We remove the assumptions of amenability in two theorems of Clark about C*-algebras of locally compact groupoids. The first result is that if the groupoid C*-algebra is GCR, or equivalently then the groupoid's orbits are locally closed. We prove the contrapositive. We begin by constructing a direct integral representation of the groupoid C*-algebra with respect to a measure on the groupoid's unit space. If the orbits are not locally closed, then there is a non-trivial ergodic measure on the unit space. We adapt a known result for transformation groups to groupoids, which shows that the direct integral representation cannot be type I if the measure on the unit space is non-trivially ergodic.
The second result is that if the groupoid C*-algebra is CCR, then the groupoid's orbits are closed. Here we show that if a representation of a stability subgroup is induced to a representation of the groupoid C*-algebra, then the induced representation is equivalent to a representation as multiplication operators acting on a vector-valued L2-space. If we assume the groupoid C*-algebra is CCR, but an orbit is not closed, then the equivalence of two representations as multiplication operators leads to a contradiction.
2017-10-10T23:50:12ZInverse problems in evolutionary biologyHiscott, Gordonhttp://hdl.handle.net/10523/74302018-02-28T01:24:14Z2017-07-07T04:02:45ZInverse problems in evolutionary biology
2017
Hiscott, Gordon
In this thesis, we explore three techniques which could be used to increase the efficiency of analyses in evolutionary genetics while still producing reasonably accurate results. The first of these methods improves the efficiency of analyses based on Markov chain Monte Carlo (MCMC) through the application of delayed acceptance sampling, an MCMC method with an additional proposal step in which an acceptance probability is computed from computationally less expensive approximate likelihoods. Rejection at the additional decision step should allow software like SNAPP (``SNP and AFLP Phylogenies") to avoid unnecessary computation of full likelihoods and, therefore, run more efficiently. The second method we discuss combines dynamic programming with classical numerical integration methods to compute likelihoods with respect to continuous trait models on trees. This method assumes explicitly known transition densities, but is efficient and has a relatively fast convergence rate. We apply the method to a threshold model which combines continuous traits with discrete observations. The third method we look at is another dynamic programming integration algorithm, except that this algorithm takes advantage of a basis function approximation of likelihood functions. This method allows for numerical solutions to PDEs to be applied directly and the use of Chebyshev polynomials as the basis functions make the method easy to implement. We apply this method to the computation of the likelihood given a genetic data set generated by diffusion processes.
2017-07-07T04:02:45ZModeling Continuous Time Series With Many ZerosWang, Yunanhttp://hdl.handle.net/10523/73972017-06-25T23:02:07Z2017-06-25T23:00:58ZModeling Continuous Time Series With Many Zeros
2017
Wang, Yunan
Earthquake activity is generally modeled using point processes as earthquake events usually occur at random times and locations. Recent studies have found it mathematically challenging and computationally complex to incorporate a point process model into a hidden Markov model to describe long-term seismicity. Given that earthquake data can be discretized in time to consider daily or hourly energy release, time series models could be a useful method for earthquake data analysis. Time series models can account for the autocorrelation of earthquakes. However, one issue that arises with the earthquake occurrence data is that there is a substantial proportion of time when no earthquake is recorded. This thesis proposes a class of two-part autoregressive (2PAR) models for continuous time series data with excess zeros. We employ a Bernoulli variable to model the excess zeros in the data, and use autoregressive processes to describe the serial correlation. Using this class of 2PAR models, we can model correlations that exist in either zeros or nonzeros in the data. We have proposed a class of residual analysis to check the goodness-of-fit of the proposed models. We also introduced a forecasting procedure using simulation to check the performance of the models.
We carried out a simulation study which shows that the estimators are unbiased and consistent, and the residual analysis and forecasting procedure for the proposed models are promising. We applied the proposed models to the energy indices obtained from the total stress release per hour from the 2010 Darfield earthquake sequence. The results reveal that the 2PAR models with serial correlation in both the presence probability and the earthquake energy indices captured the main features of the data. A retrospective forecasting experiment suggested that the proposed models provide higher information gain against a reference model.
2017-06-25T23:00:58Z