Dimension-robust and dynamical uncertainty quantification for digital twins. This project aims to develop new, mathematically grounded uncertainty quantification tools to manage stochastic interaction

Description

Dimension-robust and dynamical uncertainty quantification for digital twins. This project aims to develop new, mathematically grounded uncertainty quantification tools to manage stochastic interactions between digital twins and large-scale physical systems, enabling tighter integration of models, data, and decisions. This project expects to advance theory and algorithms of measure transport, creating new sequential inverse problem solvers to update digital twins and reliable risk estimators to support decisions driving these systems. It will develop novel dimension reduction methods to make these algorithms scalable. This should be significantly beneficial for Australian industries by providing accurate, cost-efficient tools for predicting and managing the dynamic behaviour of physical systems using digital twins.. Scheme: ARC Future Fellowships. Field: 4903 - Numerical and Computational Mathematics. Lead: Dr Tiangang Cui