{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101080"},"data":{"type":"grant-details","id":"DE260101080","attributes":{"code":"DE260101080","administering-organisation":"The University of Sydney","announcement-administering-organisation":"The University of Sydney","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-04-01","anticipated-end-date":"2029-03-31","grant-summary":"From chaos to clarity: reliable data-driven analysis of dynamical systems. Unpredictable systems such as weather and climate often exhibit patterns, like El Niño, which can have devastating impacts. The project aims to revolutionise our ability to reliably use past observations to forecast and uncover hidden patterns in systems. It will use innovative mathematics to investigate new data-driven algorithms that have quickly become very popular but are poorly understood. This will reveal the mathematical structure behind the success or failure of these algorithms. Expected outcomes include rigorous tests and guidelines for the valid use of pattern-finding algorithms, with application to critical problems, including predicting heatwaves under climate change, and optimising the effect of turbulence in aircraft design.","funding-current":525546.00,"funding-at-announcement":521368,"investigators-current":[{"title":"Dr","firstName":"Caroline","familyName":"Wormell","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-2953-6493 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Caroline","familyName":"Wormell","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0003-2953-6493 "}],"organisations-current":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":false,"code":"490302","name":"Numerical Analysis","type":"FOR20"},{"isPrimary":true,"code":"4904","name":"Pure Mathematics","type":"FOR20"},{"isPrimary":false,"code":"490409","name":"Ordinary Differential Equations, Difference Equations and Dynamical Systems","type":"FOR20"}],"socio-economic-objective":[{"code":"280118","name":"Expanding Knowledge In the Mathematical Sciences","type":"SEO20"}],"international-collaboration":["England","Finland","Germany"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project will investigate a recently-developed family of algorithms, Extended Dynamical Mode Decomposition, that can be used to make predictions and uncover key patterns in complex, unpredictable systems, such as weather and climate processes and airflow in jet engines. These algorithms are extremely popular with scientists and engineers due to many advantages over previous algorithms, but they are in many respects a \"black box\" whose success (and failure) no-one really understands. This undermines the opportunity to capitalise on these algorithms in contexts where we rely on the results being accurate.\n\nThis project will develop mathematical insight into why these algorithms work when they do, and provide ways to tell when we can trust the output of these algorithms. This will allow us to use the algorithms in settings where reliability is key, such as when designing aircraft or planning for heatwave scenarios in Australia.\n\nThe project will raise Australia's profile as a centre of research in dynamical systems and machine learning. It will attract and train talented students, expanding Australia's workforce capacity and skill base in the key area of data science. "}}}