An investigation into alternative methods for the simulation and analysis of growth models
Odiam, Lee Richard Frederick
Thesis or dissertation
- © 2019 Lee Richard Frederick Odiam. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder.
Complex systems are a rapidly increasing area of research covering numerous disciplines including economics and even cancer research, as such the optimisation of the simulations of these systems is important. This thesis will look specifically at two cellular automata based growth models the Eden growth model and the Invasion Percolation model. These models tend to be simulated storing the cluster within a simple array. This work demonstrates that for models which are highly sparse this method has drawbacks in both the memory consumed and the overall runtime of the system. It demonstrates that more modern data structures such as the HSH tree can offer considerable benefits to these models.
Next, instead of optimising the software simulation of the Eden growth model, we detail a memristive-based cellular automata architecture that is capable of simulating the Eden growth model called the MEden model. It is demonstrated that not only is this method faster, up to 12; 704 times faster than the software simulation, it also allows for the same system to be used for the simulation of both EdenB and EdenC clusters without the need to be reconfigured; this is achieved through the use of two different parameters present in the model Pmax and Pchance. Giving the model a broader range of possible clusters which can aid with Monte-Carlo simulations of the model.
Finally, two methods were developed to be able to identify a difference between fractally identical clusters; connected component labelling and convolution neural networks are the methods used to achieve this. It is demonstrated that both of these methods allow for the identification of individual Eden clusters able to classify them as either an EdenA, EdenB, or EdenC cluster, a highly nontrivial matter with current methods. It is also able to tell when a cluster was not an Eden cluster even though it fell in the fractal range of an Eden cluster. These features mean that the verification of a new method for the simulation of the Eden model could now be automated.
- Department of Computer Science, The University of Hull
- Hawick, Ken
- Sponsor (Organisation)
- University of Hull
- Qualification level
- Qualification name
- 15 MB