Data Adaptive Geophysical Inversion. The goal of this project is to develop new techniques for extracting information about the interior structure of the Earth from large geophysical data sets. These methods will be adaptive so that they allow the definition of the physical model to be constrained by the character of the data. The project will utilize advances in computational geometry, nonlinear inversion and interactive computer visualisation to extract robust information from data sets with v ....Data Adaptive Geophysical Inversion. The goal of this project is to develop new techniques for extracting information about the interior structure of the Earth from large geophysical data sets. These methods will be adaptive so that they allow the definition of the physical model to be constrained by the character of the data. The project will utilize advances in computational geometry, nonlinear inversion and interactive computer visualisation to extract robust information from data sets with variable resolving power. The resulting algorithms will be applicable to a wide range of problems in the physical sciences.Read moreRead less
Realising the promise of neural networks for practical optimisation: improving their efficiency and effectivess through chaotic dynamics and hardware implementation. Combinatorial optimisation problems such as transportation routing and assembly-line scheduling are critical to the efficiency of many industries, but their combinatorial explosion makes rapid solution difficult. Neural networks (NNs) hold much potential for rapid solution though hardware implementation, but we need to improve the q ....Realising the promise of neural networks for practical optimisation: improving their efficiency and effectivess through chaotic dynamics and hardware implementation. Combinatorial optimisation problems such as transportation routing and assembly-line scheduling are critical to the efficiency of many industries, but their combinatorial explosion makes rapid solution difficult. Neural networks (NNs) hold much potential for rapid solution though hardware implementation, but we need to improve the quality of their solutions before developing hardware. We have previously shown that the rich dynamics of chaos can improve the efficiency and effectiveness of NNs. We aim to develop new chaotic NN models, rigorously evaluate them on industrially significant problems such as those arising in manufacturing, logistics and telecommunications, and demonstrate their speed through hardware acceleration.Read moreRead less
Novel decomposition methods for large scale optimisation. This project will develop more effective problem decomposition methods that are critical for handling large scale problems (problems with up to several thousands of variables). The project will benefit practitioners from many different fields, and will put Australia at the very forefront of international research for large scale optimization.
Beyond black-box models: interaction in eXplainable Artificial Intelligence. This project addresses a key issue in automated decision making: explaining how a decision was reached by a computer system to its users. Its aim is to progress towards a new generation of explainable decision models, which would match the performance of current black-box systems while at the same time allow for transparency and detailed interpretation of the underlying logic. This project expects to generate new knowl ....Beyond black-box models: interaction in eXplainable Artificial Intelligence. This project addresses a key issue in automated decision making: explaining how a decision was reached by a computer system to its users. Its aim is to progress towards a new generation of explainable decision models, which would match the performance of current black-box systems while at the same time allow for transparency and detailed interpretation of the underlying logic. This project expects to generate new knowledge in modelling interdependencies of decision criteria using recent advances in the theory of capacities. The expected outcomes are sophisticated but tractable models in which mutual dependencies of decision rules and criteria are treated explicitly and can be thoroughly evaluated. Read moreRead less