ERC Advanced Grant · 2017
Exploring Duality for Future Data-driven Modelling
Future data-driven modelling is increasingly challenging for many systems due to higher complexity levels, such as in energy systems, environmental and climate modelling, traffic and transport, industrial processes, health, safety, and others. This requires powerful concepts and frameworks that enable the design of high quality predictive models. In this proposal E-DUALITY we will explore and engineer the potential of duality principles for future data-driven modelling. An existing example illustrating the important role of duality in this context is support vector machines, which possess primal and dual model representations, in terms of feature maps and kernels, respectively. Within this…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.