Name: CFD Training Series: Introduction to Discontinuous Galerkin Methods for Flow Problems Event type: Workshop Date: October 28, 2022 Time: 1pm - 5pm Format: Online Target Audience: HPC users Contact Person: Dr. Martin Smuda *More information* Desciption In this course, we cover the main building blocks to solve fluid flow problems using the Discontinuous Galerkin (DG) […]
Name: Training: Einführung in git und FDM mit GitLab Veranstaltungsart: Workshop Datum: 10. November 2022 Uhrzeit: 09:00 - 14:00 Uhr Format: Online Zielgruppe: Beschäftigte aus Infrastruktureinrichtungen der Hochschulen sowie Forschende, die grundlegende Kenntnisse der Versionierung-Software GitLab erwerben wollen. Wichtige Information: Es können nur eine begrenzte Anzahl von Teilnahmewünschen berücksichtigt werden. Sie erhalten zeitnah nach Ende […]
Name: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation Event type: Workshop Date: November 11, 2022 Time: 1pm - 5pm Format: Online Target Audience: HPC user Contact Person: Xiaoyu Wang *More information* Desciption In this course, the introduction to the structural properties of various turbulence modeling concepts (RANS, LES, and Hybrid RANS/LES) including associated […]
Name: Training: Visualization and Analysis of Atomistic Simulation Data in OVITO Event type: Workshop Date: November 15 to 17, 2022 Time: 1pm - 5pm Format: Online via Zoom Target Audience: HPC user Contact Person: Dr. Daniel Utt Registration *More information* Desciption Post-processing and analysis of atomistic simulations are essential steps to extract knowledge from the computed […]
Title: NHR4CES Community Workshop 2023 – Machine Learning in Computational Fluid Dynamics Event type: Workshop Date & Time: February 28, 2023, 1.30pm - 5.30pm and March 1, 2023, 9.00am - 1.30pm Format: Online Contact: office@nhr.tu-darmstadt.de Contact persons: Jonas Seng, Dr. Martin Smuda and Ludovico Nista Registration *More information* Desciption Big data and Machine Learning (ML) […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Machine Learning (ML) methods are being used frequently in research these days. However, often non-experts in ML are faced with the problem of designing and building highly complex models and ML-environments to utilize useful features that ML brings. A high invest of time is needed to acquire the knowledge required to develop ML-models and the […]
Name: Machine Learning in Combustion Date: September 21 & 22, 2023 Time: 1 pm – 5.30 pm Format: Online More Information 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 […]
