Effective and Efficient Gaussian Processes

Learn advanced techniques for implementing Gaussian processes in computational modeling and uncertainty quantification through this comprehensive workshop from the Alan Turing Institute. Explore theoretical foundations and practical applications across multiple domains including computer model emulation, Bayesian inversion, and geophysical modeling. Discover how to handle high-dimensional hierarchical models for large-scale applications and implement known boundary emulation techniques for complex computer models. Master multiscale approaches to global temperature reconstruction and efficient calibration methods for spatio-temporal models. Delve into cutting-edge topics such as deep Gaussian processes, multi-fidelity learning, and integrated emulators for multi-physics systems. Gain insights into sequential design methodologies based on mutual information for computer experiments and understand how GP emulators integrate into uncertainty quantification workflows in real-world practice. Benefit from expert presentations by leading researchers from institutions including Courant Institute, Cambridge, Edinburgh, Exeter, Amazon, UCL, and KAUST, covering both theoretical advances and pragmatic implementation strategies for effective and efficient Gaussian process applications.