DSpace Collection:http://hdl.handle.net/2440/3062014-08-28T11:06:22Z2014-08-28T11:06:22ZCarbon nanotori as traps for atoms and ionsChan, Y.Cox, B.Hill, J.http://hdl.handle.net/2440/765952014-08-19T09:15:00Z2011-12-31T13:30:00ZTitle: Carbon nanotori as traps for atoms and ions
Author: Chan, Y.; Cox, B.; Hill, J.2011-12-31T13:30:00ZPatch dynamics for macroscale modelling in one dimensionBunder, J.Roberts, A.http://hdl.handle.net/2440/719812014-08-19T11:18:33Z2011-12-31T13:30:00ZTitle: Patch dynamics for macroscale modelling in one dimension
Author: Bunder, J.; Roberts, A.
Abstract: We discuss efficient macroscale modelling of microscale systems using patch dynamics. This pilot study effectively homogenises microscale varying diffusion in one dimension. The `equation free' approach requires that the microscale model be solved only on small spatial patches. Suitable boundary conditions ensure that these patches are well coupled. By centre manifold theory, an emergent closed model exists on the macroscale. Patch dynamics systematically approximates this macroscale model. The modelling is readily adaptable to higher dimensions and to reaction-diffusion equations.
Description: Proceedings of the 10th Biennial Engineering Mathematics and Applications Conference (EMAC2011) held at University Technology Sydney in December 20112011-12-31T13:30:00ZAveraging approximation to singularly perturbed nonlinear stochastic wave equationsLv, Y.Roberts, A.http://hdl.handle.net/2440/714662014-08-19T09:14:40Z2011-12-31T13:30:00ZTitle: Averaging approximation to singularly perturbed nonlinear stochastic wave equations
Author: Lv, Y.; Roberts, A.
Abstract: An averaging method is applied to derive effective approximation to a singularly perturbed nonlinear stochastic damped wave equation. Small parameter ν > 0 characterizes the singular perturbation, and νᵅ, 0 ≤ α ≤ 1/2, parametrizes the strength of the noise. Some scaling transformations and the martingale representation theorem yield the effective approximation, a stochastic nonlinear heat equation, for small ν in the sense of distribution.2011-12-31T13:30:00ZAverage and deviation for slow-fast stochastic partial differential equationsWang, W.Roberts, A.http://hdl.handle.net/2440/714532014-08-19T09:19:52Z2011-12-31T13:30:00ZTitle: Average and deviation for slow-fast stochastic partial differential equations
Author: Wang, W.; Roberts, A.
Abstract: Averaging is an important method to extract effective macroscopic dynamics from complex systems with slow modes and fast modes. This article derives an averaged equation for a class of stochastic partial differential equations without any Lipschitz assumption on the slow modes. The rate of convergence in probability is obtained as a byproduct. Importantly, the stochastic deviation between the original equation and the averaged equation is also studied. A martingale approach proves that the deviation is described by a Gaussian process. This gives an approximation to errors of order O(ε) instead of order O(√ε) attained in previous averaging.2011-12-31T13:30:00Z