Artificial Intelligence for Decision Making: Machine learned models from simulation data

Project Member(s): Bishop, A.

Funding or Partner Organisation: NSW Department of Industry (NSW Defence Innovation Network)
NSW Department of Industry (NSW Defence Innovation Network)

Start year: 2020

Summary: The idea for this project is to evaluate how well data generated by the simulation of a model can be analysed using machine learning techniques to create a meta-model of the simulation itself. This meta-model is especially desirable if it is more efficient than the simulation in terms of time and/or computational resources.

Publications:

Bishop, AN & Del Moral, P 2023, 'Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems', SIAM/ASA Journal on Uncertainty Quantification, vol. 11, no. 2, pp. 389-425.
View/Download from: Publisher's site

Bishop, AN & Bonilla, EV 1970, 'Recurrent Neural Networks and Universal Approximation of Bayesian Filters', Proceedings of Machine Learning Research, pp. 6956-6967.

FOR Codes: Numerical and computational mathematics, Statistics, Emerging Defence Technologies

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.