57 citations · 84 across the 6 of their papers we have counts for
4 papers · 1 filter
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz +4
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Sc…
A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps
Raphael Leiteritz, Kyle Davis, Miriam Schulte +1
Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative…
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…