57 citations · 84 across the 6 of their papers we have counts for
6 papers
Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps
Kyle Davis, Raphael Leiteritz, Dirk Pflüger +1
The ability for groundwater heat pumps to meet space heating and cooling demands without relying on fossil fuels, has prompted their mass roll out in dense urban environments. In r…
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…
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…