SOURCE-LINKED INTELLIGENCE

Compositional Approximation Schemes

CORDIS · observation · Publication date unknown

thematical foundation to be able to guarantee the accuracy of the solution they produce. While this lack of reliability constitutes a major issue for many applications, I believe that, for scientific machine learning in particular, there is a promising path towards overcoming these issues, as there usually exists knowledge of the ground truth one would like to learn, e.g. that it must satisfy some partial differential equation. The goal of this project is understanding, from an approximation theory perspective, when and why such knowledge may be exploited. A defining property of neural networks is that they consist of a composition of simple building blocks. From the view of approximation theory this is a major paradigm shift, as it classically focuses on superpositional approximation, i.e. based on taking linear combinations of simple building blocks. This project aims to understand, on a fundamental structural level, how compositional approximation differs from classical superpositional approximation. Specifically it will first prove the existence of cases, in which compositional

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recordType

award

status

SIGNED

region

EU

value

183600.96

unit

EUR