Abstract:
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. In particular, we are able to rank the importance of all the interaction potential (force field) parameters. I will compare and contrast such approaches with the situation which pertains when attempts are made to replace these force fields with machine learned versions in the hope of making them more widely applicable.