Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. T ....Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. This information will be used to create a virtual biomass particle model for an in silico investigation to inform optimal process design. The framework will transform the way biomass is processed, contributing to the growth of the Australian bio-manufacturing industry by making it more productive, profitable and sustainable.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100042
Funder
Australian Research Council
Funding Amount
$339,237.00
Summary
Hybrid optimisation for coordinating autonomous trucks and drones. This project aims to build analytics for controlling a fleet of autonomous trucks and drones working in tandem to deliver retail goods and disaster relief. This project expects to develop new mathematical and artificial intelligence algorithms for routing and scheduling the vehicles and for directing the multi-modal transfer of goods between vehicles in real-time as traffic conditions change. Expected outcomes of this project inc ....Hybrid optimisation for coordinating autonomous trucks and drones. This project aims to build analytics for controlling a fleet of autonomous trucks and drones working in tandem to deliver retail goods and disaster relief. This project expects to develop new mathematical and artificial intelligence algorithms for routing and scheduling the vehicles and for directing the multi-modal transfer of goods between vehicles in real-time as traffic conditions change. Expected outcomes of this project include new theories and technologies that enable a central computer to remotely control the autonomous fleet for maximum efficiency. Benefits in transport and logistics include improved freight productivity through reducing costs and delivery times.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100674
Funder
Australian Research Council
Funding Amount
$370,237.00
Summary
New Frontiers in Large-Scale Polynomial Optimisation. Polynomial optimisation is ubiquitous in many areas of engineering and applied mathematics. The mathematical methods and algorithms used for polynomial problems of large size are not sufficiently developed, limiting their applicability for real-world problems. This project aims to develop a mathematical foundation and computational methods for large-scale polynomial optimisation. By using an innovative combination of a novel theory of algebra ....New Frontiers in Large-Scale Polynomial Optimisation. Polynomial optimisation is ubiquitous in many areas of engineering and applied mathematics. The mathematical methods and algorithms used for polynomial problems of large size are not sufficiently developed, limiting their applicability for real-world problems. This project aims to develop a mathematical foundation and computational methods for large-scale polynomial optimisation. By using an innovative combination of a novel theory of algebraic geometry and convex optimisation, this project expects to generate new knowledge and tools for solving these problems. Anticipated outcomes include a new generation of large-scale optimisation technologies, providing significant benefit to Australia's industries and international research standing.
Read moreRead less
Approximation theory of structured neural networks . Mathematical theory for deep learning has been desired due to the power applications of deep neural networks to deal with big data in various practical domains. The main difficulty lies in the structures and architectures imposed to networks designed for specific learning tasks. Neither the classical approximation theory nor the recent one for depths of ReLU neural networks can be applied due to the structures imposed for processing large dime ....Approximation theory of structured neural networks . Mathematical theory for deep learning has been desired due to the power applications of deep neural networks to deal with big data in various practical domains. The main difficulty lies in the structures and architectures imposed to networks designed for specific learning tasks. Neither the classical approximation theory nor the recent one for depths of ReLU neural networks can be applied due to the structures imposed for processing large dimensional data such as natural images of tens of thousands of dimensions. This project aims at an approximation theory for structured neural networks. We plan to establish mathematical theories for deconvolution with deep convolutional neural networks, operator learning, and spectral graph networks. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100006
Funder
Australian Research Council
Funding Amount
$444,847.00
Summary
Robust Derivative-Free Algorithms for Complex Optimisation Problems. Mathematical optimisation gives a systematic way for optimal decision-making. This project aims to develop new mathematical tools for complex optimisation problems where limited problem information is available. It will generate new foundational theories for alternative optimisation tools, introducing substantial new capability and rigour to the discipline. The project will create significant new mathematical optimisation techn ....Robust Derivative-Free Algorithms for Complex Optimisation Problems. Mathematical optimisation gives a systematic way for optimal decision-making. This project aims to develop new mathematical tools for complex optimisation problems where limited problem information is available. It will generate new foundational theories for alternative optimisation tools, introducing substantial new capability and rigour to the discipline. The project will create significant new mathematical optimisation techniques and create world-leading and publicly available software. These new techniques and software may ultimately be able to solve some of the most complex optimisation problems in research and industry, such as improving long-term climate predictions and designing 3D-printed medical implants.Read moreRead less
Next-generation methods for transport in poroelastic media with interfaces. Deformable porous structures are ubiquitous in the design of materials such as filters, sponges, and prosthetics. They often show complex mechano-chemical processes that occur across several spatio-temporal scales. To mathematically describe them requires coupled sets of nonlinear, multiphysical, and multiscale equations. This makes the design of accurate, efficient numerical methods challenging. The Fellowship aims to a ....Next-generation methods for transport in poroelastic media with interfaces. Deformable porous structures are ubiquitous in the design of materials such as filters, sponges, and prosthetics. They often show complex mechano-chemical processes that occur across several spatio-temporal scales. To mathematically describe them requires coupled sets of nonlinear, multiphysical, and multiscale equations. This makes the design of accurate, efficient numerical methods challenging. The Fellowship aims to address the mathematical characteristics encountered in poromechanics equations and their discretisation methods, and to devise novel mathematical and computational techniques for extending the analysis to cases where large deformations and the presence of interfaces and coupling with other neighbouring elements are relevant.Read moreRead less
High Dimensional Approximation, Learning, and Uncertainty. This project aims to develop next-generation computational methods for complex problems in science and engineering that have many uncertain parameters, using advanced high-dimensional strategies and deep learning to enhance computational speed. The significance of the project is that these methods will help address important applications that at present are not feasible or at the edge of feasibility. The expected outcomes are powerful me ....High Dimensional Approximation, Learning, and Uncertainty. This project aims to develop next-generation computational methods for complex problems in science and engineering that have many uncertain parameters, using advanced high-dimensional strategies and deep learning to enhance computational speed. The significance of the project is that these methods will help address important applications that at present are not feasible or at the edge of feasibility. The expected outcomes are powerful methods that will be mathematically rigorous and suitable for a wide variety of applications. The benefits are that the project will boost Australia’s position as a leader in innovation, and contribute to future developments over a wide area, from aerospace engineering to personalised computational oncology.Read moreRead less