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DTSTART;TZID=Europe/Berlin:20231102T130000
DTEND;TZID=Europe/Berlin:20231102T170000
DTSTAMP:20260822T090510
CREATED:20230928T130749Z
LAST-MODIFIED:20230928T133813Z
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SUMMARY:[:en]NHR4CES: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation[:]
DESCRIPTION:Name: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation \nDate: November 02\, 2023 \n \nTime: 1.00 pm – 5.00 pm \nFormat: online \nMore Information \n  \n\n  \nDescription\nShort abstract: \n\n\nIn this course\, the introduction to the structural properties of various turbulence modeling concepts (RANS\, LES\, and Hybrid RANS/LES) including associated equations will be given. In addition to the presentation\, the corresponding computational setup including pre-processing\, simulation implementation\, and post-processing for some illustrative flow configurations will be provided based on the open-source CFD software OpenFOAM. \n\n\n  \nClick here to register
URL:https://blog.rwth-aachen.de/itc-events/en/event/nhr4ces-cfd-training-series-introduction-to-turbulence-modeling-and-numerical-implementation/
LOCATION:Online\, Deutschland
CATEGORIES:Einmalig,HPC Events
ATTACH;FMTTYPE=image/jpeg:https://blog.rwth-aachen.de/itc-events/files/2022/09/NHR4CES_RGB.jpg
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DTSTART;TZID=Europe/Berlin:20231109T140000
DTEND;TZID=Europe/Berlin:20231110T180000
DTSTAMP:20260822T090510
CREATED:20230830T153110Z
LAST-MODIFIED:20240405T120923Z
UID:6619-1699538400-1699639200@blog.rwth-aachen.de
SUMMARY:[:en]NHR4CES: Machine Learning in Combustion[:]
DESCRIPTION:Name: Machine Learning in Combustion \nDate: November 09 & 10\, 2023\n \nTime: 2 pm – 6 pm \nFormat: Online \nMore Information \n  \n\n  \nDescription\nShort abstract: \nBig 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 been already applied to several Computational Fluid Dynamics (CFD) configurations and 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 in a hierarchical manner 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. \nThis training is designed to provide 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 architectures 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. \n  \nClick here to register\n 
URL:https://blog.rwth-aachen.de/itc-events/en/event/nhr4ces-machine-learning-in-combustion/
LOCATION:Online\, Deutschland
CATEGORIES:Einmalig,HPC Events,NHR4CES Events
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