57 citations · 220 across the 26 of their papers we have counts for
6 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…
ORSA: Outlier Robust Stacked Aggregation for Best- and Worst-Case Approximations of Ensemble Systems\
Peter Domanski, Dirk Pflüger, Jochen Rivoir +1
In recent years, the usage of ensemble learning in applications has grown significantly due to increasing computational power allowing the training of large ensembles in reasonable…
Self-Learning Tuning for Post-Silicon Validation
Peter Domanski, Dirk Pflüger, Jochen Rivoir +1
Increasing complexity of modern chips makes design validation more difficult. Existing approaches are not able anymore to cope with the complexity of tasks such as robust performan…
Octo-Tiger's New Hydro Module and Performance Using HPX+CUDA on ORNL's Summit
Patrick Diehl, Gregor Daiß, Dominic Marcello +5
Octo-Tiger is a code for modeling three-dimensional self-gravitating astrophysical fluids. It was particularly designed for the study of dynamical mass transfer between interacting…