Mathis Bode

Orcid: 0000-0001-9922-9742

According to our database1, Mathis Bode authored at least 23 papers between 2016 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2025
JuMonC: A RESTful tool for enabling monitoring and control of simulations at scale.
Future Gener. Comput. Syst., 2025

2024
nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulations.
CoRR, 2024


Visuals on the House: Optimizing HPC Workflows with No-Cost CPU Visualization.
Proceedings of the 14th IEEE Symposium on Large Data Analysis and Visualization, 2024

2023
Software and Analysis for paper: Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI.
Dataset, September, 2023

Hybrid scheme for complex flows on staggered grids and application to multiphase flows.
J. Comput. Phys., February, 2023

Scaling Computational Fluid Dynamics: In Situ Visualization of NekRS using SENSEI.
Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

AI Super-Resolution Subfilter Modeling for Multi-Physics Flows.
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.
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.
CoRR, 2022

Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors.
CoRR, 2022

Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data.
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.
CoRR, 2021

Using Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Reconstruct Mixture Fraction Statistics of Turbulent Jet Flows.
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.
J. Big Data, 2019

Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows.
CoRR, 2019

Deep learning at scale for subgrid modeling in turbulent flows.
CoRR, 2019

Deep Learning at Scale for Subgrid Modeling in Turbulent Flows: Regression and Reconstruction.
Proceedings of the High Performance Computing, 2019

A discrete mathematics approach for large scale improvement in classification training time.
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.
CoRR, 2018

Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning.
Proceedings of the High Performance Computing, 2018

2016
Extreme-Scale In Situ Visualization of Turbulent Flows on IBM Blue Gene/Q JUQUEEN.
Proceedings of the High Performance Computing, 2016

Multi-scale Coupling for Predictive Injector Simulations.
Proceedings of the High-Performance Scientific Computing, 2016


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