A fusion of advanced physics-based computation with modern statistics and data science
Data-centric engineering integrates computational modelling of physical systems, statistical analysis and the rapidly growing field of data-science to build data-driven models of complex engineering systems, such as those involving fluid flow and combustion, electrical power networks, materials and structures, and chemical processes. It draws upon disparate fundamental streams of engineering and science such as finite element and finite volume methods for computational mechanics, and Bayesian statistics and probability theory to build Digital Twins of real-world systems capable of simulation, design, uncertainty prediction and real-time control.
We are a multidisciplinary group of researchers from across the University of Sydney and external organisations with combined experience in advanced materials, structures, electrical power systems, computational fluid dynamics, nuclear physics, statistics, optimisation, control, algorithms and software design. Our objective is to build unique capability – both in research and application – for developing and using data-driven engineering models of complex engineering systems.
- Our Research – fundamental developments in new statistical and computational methods that will underpin this field, providing reliable and robust methods that scale to real-world applications.
- Global Partnerships – develop strong research and development connections with other world-leading universities in this field.
- Industry Engagement – identify and partner with key industries, companies and public institutions for areas like health, infrastructure and defence with interest in data-driven engineering methods.
- Entrepreneurial Outcomes – our ambition is to be a world-leader in data-centric engineering, providing our industry, public and academic partners with state-of-the-art software, solutions and services.
Leading research groups
Explore our research
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Led by Associate Professor Willem Vervoort, the ARC Industrial Transformation Training Centre in Data Analytics for Resources and Environments (DARE) is developing and delivering the data science skills and tools for Australia’s natural resource industries and managers; to be expert users of data and models; to quantify, explain and understand uncertainty; and to make the best possible evidence-based decisions in exploiting and stewarding the nations’ natural resources and environment.
The DARE Centre is a collaborative research centre led by the University of Sydney in partnership with the University of New South Wales and the University of Western Australia and funded under a five-year grant from the Australian Research Council in partnership with industry and government. DARE's partner organisations represent some of Australia’s leading organisations directly involved in natural resource use and management.
Core research team
Aerospace, Mechanical and Mechatronic Engineering
- Professor Sally Cripps
- Professor Qing Li
- Professor Ian Manchester
- Associate Professor Matthew Cleary
- Associate Professor Ben Thornber
- Associate Professor Dries Verstraete
- Dr Donald Dansereau
- Dr Gilad Francis
- Dr Michael Groom
- Dr Agisilaos Kourmatzis
- Dr Nandini Ramesh
- Professor Itai Einav
- Professor Anna Paradowska
- Professor Kim Rasmussen
- Associate Professor Pierre Rognon
- Associate professor Mohammad Saadatfar
- Dr Mani Khezri
- Associate Professor Hao Zhang
Electrical and Information Engineering
- Professor Jian Guo Zhu
- Associate Professor Jin Ma
- Associate Professor Gregor Verbic
- Dr Sinan Li
- Dr Mahyar Shirvanimoghaddam
Life and Environmental Sciences
Phase retrieval and design with automatic differentiation
FSA: Co-designing future high performance systems for efficiency and scalability
Design automation of power electronics: hardware and control
Experiments in multiphase and multiphysics flows
Experimentalist View: Data Processing Methods and Lessons
Data-driven design under uncertainty
Frameworks for DCE with gradient based optimisation