Deep and Multi fidelity learning with Gaussian processes: Andreas Damianou, Amazon

Uncertainty quantification (UQ) employs theoretical, numerical and computational tools to characterise uncertainty. It is increasingly becoming a relevant tool to gain better understanding of physical systems and to make better decisions under uncertainty. Realistic physical systems are usually described by numerical models, often simulated using computer code. The computationally expensive and complex codes can be replaced by inexpensive and functionally simple Gaussian Process (GP) emulators that approxim