Search Results - "Bode, Mathis"

Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs

Pan, Qilong, Abdulah, Sameh, Abduljabbar, Mustafa, Ltaief, Hatem, Herten, Andreas, Bode, Mathis, Pratola, Matthew, Fadikar, Arindam, Genton, Marc G., Keyes, David E., Sun, Ying

Application-Driven Exascale: The JUPITER Benchmark Suite

Herten, Andreas, Achilles, Sebastian, Alvarez, Damian, Badwaik, Jayesh, Behle, Eric, Bode, Mathis, Breuer, Thomas, Caviedes-Voullieme, Daniel, Cherti, Mehdi, Dabah, Adel, Sayed, Salem El, Frings, Wolfgang, Gonzalez-Nicolas, Ana, Gregory, Eric B., Mood, Kaveh Haghighi, Hater, Thorsten, Jitsev, Jenia, John, Chelsea Maria, Meinke, Jan H., Meyer, Catrin I., Mezentsev, Pavel, Mirus, Jan-Oliver, Nassyr, Stepan, Penke, Carolin, Rommer, Manoel, Sinha, Ujjwal, Vieth, Benedikt von St., Stein, Olaf, Suarez, Estela, Willsch, Dennis, Zhukov, Ilya

SC24: International Conference for High Performance Computing, Networking, Storage and Analysis

Analysis of In-cylinder Flow Structures and Turbulence in a Laboratory Scale Engine using Direct Numerical Simulations

Danciu, Bogdan A., Giannakopoulos, George K., Bode, Mathis, Frouzakis, Christos E.

Direct Numerical Simulation of Hydrogen Combustion in a Real-Size IC Engine

Danciu, Bogdan A., Giannakopoulos, George K., Bode, Mathis, Frouzakis, Christos E.

Deciphering boundary layer dynamics in high-Rayleigh-number convection using 3360 GPUs and a high-scaling in-situ workflow

Bode, Mathis, Alvarez, Damian, Fischer, Paul, Frouzakis, Christos E., Göbbert, Jens Henrik, Insley, Joseph A., Lan, Yu-Hsiang, Mateevitsi, Victor A., Min, Misun, Papka, Michael E., Rizzi, Silvio, Samuel, Roshan J., Schumacher, Jörg

Multi-cycle Direct Numerical Simulations of a Laboratory Scale Engine: Evolution of Boundary Layers and Wall Heat Flux

Danciu, Bogdan A.^{Aff1, IDs1049402400576w_cor1}, Giannakopoulos, George K., Bode, Mathis, Frouzakis, Christos E.

Visuals on the House: Optimizing HPC Workflows with No-Cost CPU Visualization

Mateevitsi, Victor A., Sewell, Andres, Bode, Mathis, Fischer, Paul, Gobbert, Jens Henrik, Insley, Joseph A., Kavroulakis, Ioannis, Konioris, Damaskinos, Lan, Yu-Hsiang, Min, Misun, Papageorgiou, Dimitrios, Papka, Michael E., Petruzza, Steve, Rizzi, Silvio, Tomboulides, Ananias

2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)

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

Kerkemeier, Stefan, Frouzakis, Christos E., Tomboulides, Ananias G., Fischer, Paul, Bode, Mathis

No sustained mean velocity in the boundary region of plane thermal convection

Samuel, Roshan J., Bode, Mathis, Scheel, Janet D., Sreenivasan, Katepalli R., Schumacher, Jörg

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

Mateevitsi, Victor A., Bode, Mathis, Ferrier, Nicola, Fischer, Paul, Göbbert, Jens Henrik, Insley, Joseph A., Lan, Yu-Hsiang, Min, Misun, Papka, Michael E., Patel, Saumil, Rizzi, Silvio, Windgassen, Jonathan

Influence of adversarial training on super-resolution turbulence reconstruction

Nista, Ludovico, Schumann, Christoph David Karl, Bode, Mathis, Grenga, Temistocle, MacArt, Jonathan F., Attili, Antonio, Pitsch, Heinz

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

Bode, Mathis

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

Bode, Mathis

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

Bode, Mathis, Gauding, Michael, Goeb, Dominik, Falkenstein, Tobias, Pitsch, Heinz

Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning

Bode, Mathis, Gauding, Michael, Göbbert, Jens Henrik, Liao, Baohao, Jitsev, Jenia, Pitsch, Heinz

JuMonC: A RESTful tool for enabling monitoring and control of simulations at scale

Witzler, Christian ^a, Guimarães, Filipe Souza Mendes ^a, Mira, Daniel ^b, Anzt, Hartwig ^{c, d}, Göbbert, Jens Henrik ^a, Frings, Wolfgang ^a, Bode, Mathis ^{a, ⁎}

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

Yadav, Sumedh, Bode, Mathis

2019 IEEE International Conference on Big Data (Big Data)

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

Bhati, Agastya P., Wan, Shunzhou, Alfè, Dario, Clyde, Austin R., Bode, Mathis, Tan, Li, Titov, Mikhail, Merzky, Andre, Turilli, Matteo, Jha, Shantenu, Highfield, Roger R., Rocchia, Walter, Scafuri, Nicola, Succi, Sauro, Kranzlmüller, Dieter, Mathias, Gerald, Wifling, David, Donon, Yann, Di Meglio, Alberto, Vallecorsa, Sofia, Ma, Heng, Trifan, Anda, Ramanathan, Arvind, Brettin, Tom, Partin, Alexander, Xia, Fangfang, Duan, Xiaotan, Stevens, Rick, Coveney, Peter V.

Acceleration of complex high-performance computing ensemble simulations with super-resolution-based subfilter models

Bode, Mathis, Göbbert, Jens Henrik

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

Bode, Mathis, Gauding, Michael, Lian, Zeyu, Denker, Dominik, Davidovic, Marco, Kleinheinz, Konstantin, Jitsev, Jenia, Pitsch, Heinz