Inverse Problems and Optimisation Workshop: 6th - 7th May 2021

Inverse problems are concerned with the recovery of the parameters of a forward model given observations of data that it describes. Such problems arise in almost all fields of science when details of a postulated model, such as maps of physical properties and/or their classification into identifiable objects, have to be determined from a set of observed data.The inverse problem topic is highly cross-disciplinary, both within mathematics, encompassing aspects of pure, applied and statistics, and across subjects, including physical sciences, engineering and biology to name only a few. Inverse problems increasingly consider mappings between solutions and data1in high numbers of dimensions (e.g. three in space plus time plus wavelength). Direct representations easily exceed existing computational and memory resources, and necessitate appropriate design of data structures and algorithms. In parallel, machine learning methods designed for “big data” problems are proving useful in developing data reduction approaches and representation of appropriate priors. Some of the topics of this planned workshop includescheduling and optimising parallelism for multiple forward solves as part of a nonlinear inverse problem on exascale architectures; the combination of inference-based machine learning techniques and classical model based inverse problems at scale, and their often differing hardware requirements (e.g. GPU vs CPU); using exascale computing to include uncertainty in the formulation of inverse problems.

The workshop will take place on Zoom and is organised by Prof Timo Betcke (t.betcke@ucl.ac.uk) and Prof Simon Arridge (s.arridge@ucl.ac.uk).

Please click on the 'Book Now' link above for further information about this workshop and to register for this event.