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Mathis Bode
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2020 – today
- 2024
- [i12]Andreas Herten, Sebastian Achilles, Damian Alvarez, Jayesh Badwaik, Eric Behle, Mathis Bode, Thomas Breuer, Daniel Caviedes-Voullième, Mehdi Cherti, Adel Dabah, Salem El Sayed, Wolfgang Frings, Ana Gonzalez-Nicolas, Eric B. Gregory, Kaveh Haghighi Mood, Thorsten Hater, Jenia Jitsev, Chelsea Maria John, Jan H. Meinke, Catrin I. Meyer, Pavel Mezentsev, Jan-Oliver Mirus, Stepan Nassyr, Carolin Penke, Manoel Römmer, Ujjwal Sinha, Benedikt von St. Vieth, Olaf Stein, Estela Suarez, Dennis Willsch, Ilya Zhukov:
Application-Driven Exascale: The JUPITER Benchmark Suite. CoRR abs/2408.17211 (2024) - [i11]Stefan Kerkemeier, Christos E. Frouzakis, Ananias Tomboulides, Paul F. Fischer, Mathis Bode:
nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulations. CoRR abs/2409.06404 (2024) - 2023
- [j3]Mathis Bode, Abhishek Deshmukh, Tobias Falkenstein, Seongwon Kang, Heinz Pitsch:
Hybrid scheme for complex flows on staggered grids and application to multiphase flows. J. Comput. Phys. 474: 108478 (2023) - [c8]Mathis Bode
:
AI Super-Resolution Subfilter Modeling for Multi-Physics Flows. PASC 2023: 15:1-15:10 - [c7]Victor A. Mateevitsi
Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI. SC Workshops 2023: 862-867 - [i10]Victor A. Mateevitsi, Mathis Bode, Nicola J. Ferrier, Paul F. Fischer, Jens Henrik Göbbert, Joseph A. Insley, Yu-Hsiang Lan, Misun Min, Michael E. Papka, Saumil Patel, Silvio Rizzi, Jonathan Windgassen:
Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI. CoRR abs/2312.09888 (2023) - 2022
- [j2]Fabian Fröde
A three-dimensional cell-based volume-of-fluid method for conservative simulations of primary atomization. J. Comput. Phys. 465: 111374 (2022) - [i9]Mathis Bode, Michael Gauding, Jens Henrik Göbbert, Baohao Liao, Jenia Jitsev, Heinz Pitsch:
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning. CoRR abs/2210.16110 (2022) - [i8]Mathis Bode, Michael Gauding, Dominik Goeb, Tobias Falkenstein, Heinz Pitsch:
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data. CoRR abs/2210.16206 (2022) - [i7]Mathis Bode:
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors. CoRR abs/2210.16219 (2022) - [i6]Mathis Bode:
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow. CoRR abs/2210.16248 (2022) - 2021
- [c6]Michael Gauding, Mathis Bode:
Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows. ISC Workshops 2021: 138-153 - [i5]Agastya P. Bhati, Shunzhou Wan, Dario Alfè, Austin R. Clyde, Mathis Bode, Li Tan, Mikhail Titov, André Merzky, Matteo Turilli, Shantenu Jha, Roger R. Highfield, Walter Rocchia, Nicola Scafuri, Sauro Succi, Dieter Kranzlmüller, Gerald Mathias, David Wifling, Yann Donon, Alberto Di Meglio, Sofia Vallecorsa, Heng Ma, Anda Trifan, Arvind Ramanathan, Tom Brettin, Alexander Partin, Fangfang Xia, Xiaotan Duan, Rick Stevens, Peter V. Coveney:
Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers. CoRR abs/2103.02843 (2021)
2010 – 2019
- 2019
- [j1]Sumedh Yadav
A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training. J. Big Data 6: 96 (2019) - [c5]Sumedh Yadav, Mathis Bode:
A discrete mathematics approach for large scale improvement in classification training time. IEEE BigData 2019: 253-259 - [c4]Mathis Bode
Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction. ISC Workshops 2019: 541-560 - [i4]Sumedh Yadav, Mathis Bode:
A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training. CoRR abs/1907.10421 (2019) - [i3]Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch:
Deep learning at scale for subgrid modeling in turbulent flows. CoRR abs/1910.00928 (2019) - [i2]Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows. CoRR abs/1911.11380 (2019) - 2018
- [c3]Mathis Bode
Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. ISC Workshops 2018: 614-623 - [i1]Michael Gauding, Lipo Wang, Jens Henrik Göbbert, Mathis Bode, Luminita Danaila, Emilien Varea:
On the self-similarity of line segments in decaying homogeneous isotropic turbulence. CoRR abs/1809.07539 (2018) - 2016
- [c2]Mathis Bode, Marco Davidovic, Heinz Pitsch:
Multi-scale Coupling for Predictive Injector Simulations. JHPCS 2016: 96-108 - [c1]Jens Henrik Göbbert
Extreme-Scale In Situ Visualization of Turbulent Flows on IBM Blue Gene/Q JUQUEEN. ISC Workshops 2016: 45-55
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