ERC Consolidator Grant · 2025
Phase Space Foundations for Scientific Machine Learning
Science and engineering have long relied on interpretable, parsimonious models of signals and systems. Today there is a shift to highly-parameterized neural networks trained on massive datasets. This often makes it hard to understand what is taken from physics and what from data, and what mechanisms determine when the methods succeed or fail. There is a need for solid foundations for scalable and interpretable scientific machine learning. A key idea is to work in phase spaces inspired by Hamilton’s and Boltzmann’s formalisms. Phase spaces lift problems into higher-dimensional representations but in return render descriptions of a broad range of phenomena simple and interpretable. They…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.