Direct Numerical Simulation of Fluid Turbulence at Extreme Scale with psOpen. - Jens Henrik Göbbert; Michael Gauding; Cedrick Ansorge; Bernd Hentschel 001; Torsten W. Kuhlen; Heinz Pitsch (PARCO, 2015)

Framework for Sharing of Highly Resolved Turbulence Simulation Data. - Bastian Tweddell; Jens Henrik Göbbert; Michael Gauding; Benjamin Weyers; Björn Hagemeier (JHPCS, 2016)

Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. - Mathis Bode; Michael Gauding; Jens Henrik Göbbert; Baohao Liao; Jenia Jitsev; Heinz Pitsch (ISC Workshops, 2018)

On the self-similarity of line segments in decaying homogeneous isotropic turbulence. - Michael Gauding; Lipo Wang; Jens Henrik Göbbert; Mathis Bode; Luminita Danaila; Emilien Varea (CoRR, 2018)

Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction. - Mathis Bode; Michael Gauding; Konstantin Kleinheinz; Heinz Pitsch (ISC Workshops, 2019)

Deep learning at scale for subgrid modeling in turbulent flows. - Mathis Bode; Michael Gauding; Konstantin Kleinheinz; Heinz Pitsch (CoRR, 2019)

Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows. - Mathis Bode; Michael Gauding; Zeyu Lian; Dominik Denker; Marco Davidovic; Konstantin Kleinheinz; Jenia Jitsev; Heinz Pitsch (CoRR, 2019)

Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows. - Michael Gauding; Mathis Bode (ISC Workshops, 2021)

Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data. - Mathis Bode; Michael Gauding; Dominik Goeb; Tobias Falkenstein; Heinz Pitsch (CoRR, 2022)