Schlagwort: ‘HPC’
Version Control with Git – Satellite
Version Control with Git – SatelliteLocation: RWTH Aachen UniversityWednesday, 12 Nov. 2025 at 13:00 to This course offers a practical introduction to version control with Git. It is aimed at beginners with no prior knowledge as well as people who already use Git but would like to better understand the underlying concepts. The event is part of the HPC.NRW training program and will be held in a “reverse hybrid” format: The lecturer is connected virtually (main location at the University of Bonn), while participants on site at RWTH Aachen University are supported by assistants. Topics: · Background The course complements the Python introductory course in terms of content and takes place in the same week. Participation is free of charge, but registration via the event page is required. The course language is English, and presentation slides will be made available afterwards. Basic knowledge of using the Linux console is helpful.
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Introduction to Programming with Python – Satellite
Introduction to Programming with Python – SatelliteLocation: RWTH Aachen UniversityMonday, 10 Nov. 2025 at 15:00 to
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Introduction to Finite Element Methods for Flow Problems
Name: Introduction to Finite Element Methods for Flow Problems
Date: October 28, 2025 – 01.00 pm – 05.00 pm
Format: online
Description
Short abstract:
NHR4CES – Numerical Methods for Combustion
Name: Numerical Methods for Combustion
Date: December 5, 2025, 2pm – 6pm;
Format: online
Description
Short abstract:
In the face of climate change and its undeniable threats to humanity, the urgency for transitioning to CO2-free or carbon-neutral energy systems is paramount. While the future of energy conversion will be largely dominated by renewable sources, such as wind and solar power, combustion technologies will continue to play a crucial role in meeting the world’s growing energy demands, especially in the near term. The expected rise in alternative fuels—such as biofuels, e-fuels, and green hydrogen—demands a deeper understanding of combustion processes to ensure cleaner, safer, and more efficient energy generation. Accurately simulating combustion, however, is a formidable challenge due to its inherently multi-physics nature, involving complex interactions between turbulence, chemistry, and multi-species flows.
This workshop will focus on advanced numerical methods for modeling and simulating combustion phenomena in high-performance computing environments. Participants will gain insights into cutting-edge techniques and numerical methods, covering topics such as combustion closure modeling, load-balancing techniques, soot formation, and more. The workshop aims to gather different research groups, providing a venue to exchange new ideas, discuss challenges, and expose this new research field to a broader community.
NHR4CES – Machine Learning in Combustion
Name: Machine Learning in Combustion
Date: March 12 & 13, 2026
Format: online
Description
Short abstract:
Big data and Machine Learning (ML) are driving comprehensive economic and social transformations and are rapidly becoming a core technology for scientific computing, with numerous opportunities to advance different research areas, such as combustion modeling. The combination of combustion applications with ML has already been applied to several Computational Fluid Dynamics (CFD) configurations. It is a promising research direction with the potential to enable the advancement of so far unsolved problems, thanks to the ability of deep models to learn hierarchically with little to no need for prior knowledge. However, this approach presents a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of relevant features, enabling deeper insight into the physics involved in complex natural processes.
This training is designed to provide a basic background on machine learning applications, highlighting some of the areas of the highest potential impact. Emerging ML areas that are promising for combustion modeling, such as reduced-order modeling advancements, versatile neural network architecture developments, as well as some potential limitations, will be discussed. The workshop aims to gather different research groups, providing a venue to exchange new ideas, discuss challenges, and expose this new research field to a broader community.
NHR4CES – Introduction to Machine Learning and Deep Learning
Name: Introduction to Machine Learning and Deep Learning
Date: October 09, 2025
Format: online
Description
Short abstract:
Not only in economics Machine- and Deep-Learning (ML/DL) are inherently used to solve highly complex problems in a data-driven way, but also the scientific community has many use-cases in which ML/DL are useful, e.g. to discover hidden patterns or replace computationally heavy simulations with data-driven approaches. The participants will learn how to design ML-models by themselves and will learn about possible pitfalls when applying ML in the real world.
NHR4CES – Distributed- and Federated Learning
Name: Distributed- and Federated Learning
Date: June 05, 2025
Format: online
Description
Short abstract:
Today more data is gathered than ever before. This does not only hold for economic data, but also for research-data. At the same time, the need for ML-methods is higher than ever in research and other domains. Often the datasets used in ML-tasks are too big to be processed by a single work-station or even a single server. Distributed- and Federated Learning help to handle large datasets in a distributed way, even with privacy-guarantees in the case of Federated Learning. This workshop will introduce methods of both, Distributed Learning and Federated Learning by giving both, a theoretical and practical view on these learning-frameworks.
NHR4CES – CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation
Name: Introduction to Turbulence Modeling and Numerical Implementation
Date: 20.05.2025, 1.00 pm – 5.00 pm
Format: online
Description
Short abstract:
NHR4CES – CFD Training Series: Efficient HPC implementation for Lagrangian particle tracking
Name: CFD Training Series: Efficient HPC implementation for Lagrangian particle tracking
Date: 13.05.2025, 1.00 pm – 5.00 pm
Format: hybrid
Description
Short abstract:
NHR4CES – Materials Science with Advanced Data Management and Data Science Techniques
Name: Materials Science with Advanced Data Management and Data Science Techniques
Date: May 07-08, 2025
Format: Online
Description
We invite you to our NHR4CES Community Workshop 2025! This year’s topic is Materials Science with Advanced Data Management and Data Science Techniques. The online workshop is organized by SDL Materials Design, CSG Data Management and CSG Data Science and Machine Learning. The workshop will take place on May 07, 1pm-5pm and May 08, 9am-1pm.
With the increasing computational power, an abundance of data has become available in different research fields. To handle such an ever increasing amount of data, tools such as data management, machine learning and/or workflow managers receive increasing attention. In the field of materials science such approaches allow to investigate a wide range of materials and their properties in a systematic manner. The workflow managers are used in high-throughput calculations leading to the generation of huge amount of data. Although, high-throughput calculations are limited by the structure size. Structures with >10^3 atoms are limited by the computational resources. Researchers use the generated data and/or intelligent data mining as an input to machine learning techniques to investigate material properties, beyond limitations imposed by computational resources.
Additionally, through the collection of rich metadata in all steps of the workflow, results can be easily reproduced and reused by other researchers. This workshop is going to cover major parts of a data life cycle from generation of data via automatized workflow, their collection using data manager solutions, and their processing and/or re-using via application of machine learning while following the FAIR data principles.
Events
