ERC Starting Grant · 2020
Physics-constrained adaptive learning for multi-physics optimization
About a hundred trillion bytes of data has been created in the world while reading this sentence. Central to big data is machine learning, which is an automated way of transforming information into empirical knowledge. Machine learning techniques have been applied to some fluid mechanics problems with success, but there are still three big open questions: Do machine learning algorithms scale to engineering configurations? (Are they robust?); Can we gain physical insight into the solutions? (Are they interpretable?); Can we extrapolate knowledge to other configurations, such as multi-physics problems? (Are they generalizable?). Fluid mechanics modelling has been historically enabled by both…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.