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DTSTART;TZID=Europe/Berlin:20231109T090000
DTEND;TZID=Europe/Berlin:20231110T130000
DTSTAMP:20260504T052907
CREATED:20230830T154026Z
LAST-MODIFIED:20240405T120918Z
UID:6626-1699520400-1699621200@blog.rwth-aachen.de
SUMMARY:[:en]NHR4CES: Introduction to Machine Learning and Deep Learning[:]
DESCRIPTION:Der Eintrag ist nur auf English verfügbar. Name: Introduction to Machine Learning and Deep Learning \nDate: November 09\, 2023 & November 10\, 2023\n \nTime: 9 a.m to 1 p.m. \nFormat: Online \nMore Information \n  \n\n  \nDescription\nShort abstract:  \nNot 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. \n  \nClick here to register\n 
URL:https://blog.rwth-aachen.de/itc-events/event/nhr4ces-introduction-to-machine-learning-and-deep-learning/
LOCATION:Online\, Deutschland
CATEGORIES:Einmalig,HPC Events,NHR4CES 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
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CREATED:20230830T153110Z
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UID:6619-1699538400-1699639200@blog.rwth-aachen.de
SUMMARY:[:en]NHR4CES: Machine Learning in Combustion[:]
DESCRIPTION:Der Eintrag ist nur auf English verfügbar. 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/event/nhr4ces-machine-learning-in-combustion/
LOCATION:Online\, Deutschland
CATEGORIES:Einmalig,HPC Events,NHR4CES Events
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