57 citations · 84 across the 6 of their papers we have counts for
3 papers · 1 filter
Surrogate-data-enriched Physics-Aware Neural Networks
Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk +1
Neural networks can be used as surrogates for PDE models. They can be made physics-aware by penalizing underlying equations or the conservation of physical properties in the loss f…
How to Avoid Trivial Solutions in Physics-Informed Neural Networks
Raphael Leiteritz, Dirk Pflüger
The advent of scientific machine learning (SciML) has opened up a new field with many promises and challenges in the field of simulation science by developing approaches at the int…
Learning Free-Surface Flow with Physics-Informed Neural Networks
Raphael Leiteritz, Marcel Hurler, Dirk Pflüger
The interface between data-driven learning methods and classical simulation poses an interesting field offering a multitude of new applications. In this work, we build on the notio…