Eray is an engineer and data scientist with PhD in computational sciences working at Jülich Supercomputing Centre (JSC). His main research interests lie in AI-based engineering solutions exploiting exascale HPC systems.
proudly presenting AI4HPC, an open-source library to apply state-of-the-art AI models for complex engineering use-cases on HPC systems developed and maintained by him during his time in CoE RAISE@JSC
space
some experimental projects can be found on his GitHub
space
some old example projects:
contact Eray via
Current Project(s)
Research on AI– and simulation-based engineering at Exascale (CoE RAISE)
Previous Projects
massively parallel CFD framework development for Chemical & Pharmaceutical & Energy industries
turbulence and chemistry interaction models
performance predictions of nano-particle synthesis
talks
- Reconstruction of near-wall quantities from filtered flows using Convolutional Defiltering Model, ParCFD (2023), Cuenca/Ecuador
- Parallel and scalable deep learning to reconstruct actuated turbulent boundary layer flows. Part II: autoencoder training on HPC systems, ParCFD (2022), Alba/Italy
- Fully resolved auto-igniting transient jet flame simulation, 22. Results and Review Workshop of the HLRS (2019), Stuttgart/Germany
- Numerical study on pulsed jet flames: tabulated methods against direct chemistry, 29. Deutscher Flammentag (2019), Bochum/Germany
- LES of a pulsating jet flame in a hot co-flow, ETMM 12 (2018), Montpellier/France
- Statistical analysis of pulsating methane flames issued into hot co-flow by LES with FGM, JMGICS (2018), Sorrento/Italy
- Investigation of non-premixed piloted methane flames by LES with flamelet generated manifolds, MCS 10 (2017), Naples/Italy
- Studying transient jet flames by high-resolution LES using premixed flamelet chemistry, DLES 11 (2017), Pisa/Italy
publications
- M. Aach, R. Sarma, E. Inanc, M. Riedel and A. Lintermann, Accelerating hyperparameter optimization algorithms with mixed precision, SC-W’23 (2023)
- M. Aach, E. Inanc, R. Sarma, M. Riedel and A. Lintermann, Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks, J. Big Data 10 (2023)
- S.-J. Baik, E. Inanc, M. Klein and A. Kempf, Lagrangian filtered density function modelling of a turbulent stratified flame combined with flamelet approach, Phy. Fluids 34 (2022)
- L. Engelmann, J. Hasslberger, E. Inanc, M. Klein and A. Kempf, A-posteriori assessment of Large-Eddy Simulation subgrid-closures for momentum and scalar fluxes in a turbulent premixed burner experiment, Comp. Fluids 240 (2022)
- J. Pareja, T. Lipkowicz, E. Inanc, C. D. Carter, A. Kempf and I. Boxx, An experimental/numerical investigation of non-reacting turbulent flow in a piloted premixed Bunsen burner, EoF 63 (2022)
- E. Inanc, A. Kemp and N. Chakraborty, Scalar gradient and flame propagation statistics of a flame-resolved laboratory-scale turbulent stratified burner simulation, C&F 238 (2022)
- E. Inanc, Numerical simulation of pulsed and stratified combustion, PhD Thesis, Universität Duisburg-Essen (2021)
- E. Inanc and A. Kempf, Fully Resolved Auto-Igniting Transient Jet Flame Simulation, High Performance Computing in Science and Engineering ’19 249-262 (2021)
- E. Inanc, A. Kempf and N. Chakraborty, Effect of sub-grid wrinkling factor modelling on the Large Eddy Simulation of turbulent stratified combustion, CTM 25 (2021)
- P. Gruhlke, E. Inanc, R. Mercier, B. Fiorina and A. Kempf, A simple post-processing method to correct species predictions in artificially thickened turbulent flames, Proc. Comb. Inst. 38 (2020)
- E. Inanc, J. T. Lipkowicz and A. Kempf, Detailed simulations of the DLR auto-igniting pulsed jet experiment, Fuel 284 (2020)
- E. Inanc, N. Chakraborty and A. Kempf, Analysis of mixture stratification effects on unstrained laminar flames, C&F 219 (2020)
- E. Inanc and A. Kempf, Numerical study of a pulsed auto-igniting jet flame with detailed tabulated chemistry, Fuel 252 (2019)
- E. Inanc, F. Proch and A. Kempf, Studying transient jet flames by high-resolution LES using premixed flamelet chemistry, DLES XI:237-243 (2019)
- E. Inanc, M. T. Nguyen, S. A. Kaiser and A. Kempf, High-resolution LES of a starting jet, Comp. Fluids 140 (2016)
(PDFs can be found in ResearchGate)
posters
- E. Inanc, R. Sarma, M. Aach, R. Sedona, A. Lintermann, AI4HPC: library to train AI models on HPC Systems using CFD datasets, NeurIPS 23 (2023), New Orleans/USA
- E. Inanc, I. Wlokas, A. M. Kempf, Large-Eddy Simulation of auto-igniting pulsating methane flames with multi-dimensional tabulated chemistry, Combustion Symposium 37 (2018), Dublin/Ireland
- E. Inanc, A. M. Kempf, Combustion regime investigations of turbulent flames by flame-resolved simulations, TNF 14 (2018), Dublin/Ireland
- E. Inanc, A. M. Kempf, Large Eddy Simulation of pulsating jet flames with detailed tabulated chemistry, 28. Deutscher Flammentag (2017), Darmstadt/Germany
- E. Inanc, F. Proch, A. M. Kempf, Investigation of the auto-ignition of an impulsively started methane jet emitting into a vitiated co-flow, Future Fuels Workshop (2016), Thuwal/Saudi-Arabia
(PDFs can be found in ResearchGate)
awards
- Gauss GCS large scale project award of 48 million CPUh in High-Performance Computing Center Stuttgart (HazelHen, Cray XC40): 2018 – 2019 (Kz: 44141 GCS-JFLA)
certificates
- NVIDIA DLI Course: Fundamentals of Deep Learning for Computer Vision
- NVIDIA DLI Course: Fundamentals of Deep Learning for Multiple Data Types
- RAISE: Massively Parallel GPU Computing with CUDA: Introduction
- FZJ: Programming in C++
- FZJ: Introduction to parallel programming with MPI and OpenMP
Eray Inanc 