Software

Development and support

AI for HPC
This open-source software is developed with CoE RAISE and provides examples on the training to reconstruct actuated Turbulent Boundary Layer (TBL) datasets using Convolutional AutoEncoder (CAE).
Repository: https://gitlab.jsc.fz-juelich.de/CoE-RAISE/FZJ/ai-for-hpc-oa
Contact: Rakesh Sarma

Multiphysics Aerodynamisches Institut Aachen (m-AIA)
The simulation framework m-AIA (previsouly know as Zonal Flow Solver - ZFS) is developed by the Institute of Aerodynamics and Chair of Fluid Dynamics, RWTH Aachen University. The SDL Fluids & Solids Engineering contributes with own developments to the code and supports scaling, tuning, and porting activities.
Repository: https://git.rwth-aachen.de/aia/m-AIA/m-AIA
Contact: Andreas Lintermann, Sohel Herff, Seong-Ryong Koh

Multi-Component Flow Code (MFC)
The SDL supports the open-source MFC code with respect to scaling, tuning, and porting activies. Furthermore, MFC is used for several applications in the SDL.
Official website: https://mflowcode.github.io/
Contact: Luis Cifuentes

amrexLB
This simulation framework is developed by the SDL Fluids & Solids Engineering and is based on AMReX. It implements a high scaling lattice-Boltmann solver for fluid mechanics.
Contact: Andreas Lintermann

OpenFOAM
The SDL Fluids & Solids Engineering supports the intallation and run of OpenFOAM on JSC's HPC systems, and the user community in general.
Contact: Seong-Ryong Koh, Bernd Körfgen

Code_Saturne
The SDL Fluids & Solids Engineering supports the intallation and run of Code_Saturne on JSC's HPC systems, and the user community in general.
Official website: https://www.code-saturne.org/
Contact: Seong-Ryong Koh, Bernd Körfgen

itwinai
This library is a powerful Python toolkit designed to help scientists and researchers streamline AI and machine learning workflows, specifically for digital twin applications. It is developed in the interTwin project and provides easy-to-use tools for distributed training, hyper-parameter optimization on HPC systems, and integrated ML logging, reducing engineering overhead and accelerating research.
Repository: https://github.com/interTwin-eu/itwinai
Contact: Rakesh Sarma, Oleksandr Krochak