57 citations · 84 across the 6 of their papers we have counts for
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cs.LG2021★ 1 cited
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…
cs.LG2021★ 8 cited
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…