5th IMA Conference on Inverse Problems from Theory to Application

Inverse problems remain at the heart of scientific discovery and technological innovation, spanning fields as diverse as medical and satellite imaging, biology, astronomy, geophysics, environmental sciences, computer vision, energy, finance, and defence. Fundamentally, these problems involve using a mathematical or physical model "backwards" to infer a quantity of interest from the observed effects it produces.

Topics: Machine Learning & Data-Driven Methods, Deep learning for inverse problems, learned regularisation, neural operators, unrolled networks, Big Data, Statistics & Optimisation: Large-scale inverse problems, Bayesian inference, uncertainty quantification, data assimilation, stochastic algorithms, Mathematical Theory, Analysis, inverse problems, stability, uniqueness, and regularisation theory. Imaging & Applications, Mathematical and computational imaging, tomography, and wider applications in science, medicine, and engineering.