Mathis Bode

According to our database, Mathis Bode authored at least 18 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Hybrid scheme for complex flows on staggered grids and application to multiphase flows.

Mathis Bode, Abhishek Deshmukh, Tobias Falkenstein, Seongwon Kang, Heinz Pitsch

J. Comput. Phys., February, 2023

Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI.

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

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

AI Super-Resolution Subfilter Modeling for Multi-Physics Flows.

Mathis Bode

Proceedings of the Platform for Advanced Scientific Computing Conference, 2023

2022

A three-dimensional cell-based volume-of-fluid method for conservative simulations of primary atomization.

Fabian Fröde, Temistocle Grenga, Vincent Le Chenadec, Mathis Bode, Heinz Pitsch

J. Comput. Phys., 2022

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.

Mathis Bode

CoRR, 2022

Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors.

Mathis Bode

CoRR, 2022

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

2021

Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers.

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

CoRR, 2021

Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows.

Michael Gauding, Mathis Bode

Proceedings of the High Performance Computing - ISC High Performance Digital 2021 International Workshops, Frankfurt am Main, Germany, June 24, 2021

2019

A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training.

Sumedh Yadav, Mathis Bode

J. Big Data, 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

Deep learning at scale for subgrid modeling in turbulent flows.

Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch

CoRR, 2019

Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction.

Mathis Bode, Michael Gauding, Konstantin Kleinheinz, Heinz Pitsch

Proceedings of the High Performance Computing, 2019

A discrete mathematics approach for large scale improvement in classification training time.

Sumedh Yadav, Mathis Bode

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

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

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

Proceedings of the High Performance Computing, 2018

2016

Extreme-Scale In Situ Visualization of Turbulent Flows on IBM Blue Gene/Q JUQUEEN.

Jens Henrik Göbbert, Mathis Bode, Brian J. N. Wylie

Proceedings of the High Performance Computing, 2016

Multi-scale Coupling for Predictive Injector Simulations.

Mathis Bode, Marco Davidovic, Heinz Pitsch

Proceedings of the High-Performance Scientific Computing, 2016