SOURCE-LINKED INTELLIGENCE

Phase Space Foundations for Scientific Machine Learning

CORDIS · observation · Publication date unknown

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 reveal the universal local structure which is key for efficient computation, learning, and interpretability. In PhaseShift, we will: - Develop theory and designs for operators that leverage

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recordType

award

status

SIGNED

region

EU

value

1995957

unit

EUR