output
20112022
most citedInformation transfer between turbulent boundary layer and porous media

38 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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cs.LG2022★ 6 cited

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs

Birgit Hillebrecht, Benjamin Unger

Prediction error quantification in machine learning has been left out of most methodological investigations of neural networks, for both purely data-driven and physics-informed app…

cs.LG2022★ 11 cited

Certified machine learning: A posteriori error estimation for physics-informed neural networks

Birgit Hillebrecht, Benjamin Unger

Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust…