Keynote Speaker
Prof. Peter Coveneyย
Centre for Computational Science, Chemistry Department, University College of London (UCL)
https://www.ucl.ac.uk/chemistry/people/peter-coveney
Title: Uncertainty quantification for high-dimensional parameter spaces: physics-based versus artificial intelligence models
Summary: I will discuss the quantification of uncertainty in predictive models arising in physics-based models and models based on machine-learning. Applications will include predictions of the impact of pandemics, the design of advanced materials, discovery of new drugs and the behaviour of turbulent fluids. The curse of dimensionality has hitherto circumscribed the systematic study of more complex natural and artificial systems but the advent of scalable approaches is now starting to change things. A paradigm case which is widely used within the scientific community across all fields from physics and chemistry to materials, life and medical sciences is classical molecular dynamics. I will describe how we are now able to make global rankings of the sensitivity of quantities of interest to the many hundreds to thousands of parameters which are used in these models. Virtually all approaches to uncertainty quantification make use of large scale ensemble simulations and these typically require access to very sizeable supercomputers, among which is Frontier, on which we are currently planning some major UQ campaigns in collaboration with colleagues at Oak Ridge Leadership Computing Facility.
Media:
Simulating the pandemic: What COVID forecasters can learn from climate models
Quantifying the uncertainty of CovidSim
Literature:
W. Edeling, H. Arabnejad, R. Sinclair, D. Suleimenova, K. Gopalakrishnan, B. Bosak, D. Groen, I. Mahmood, D. Crommelin, P. Coveney, “Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code”, Nat Comput Sci 1, 128โ135 (2021),ย DOI:10.1038/s43588-021-00028-9
