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 […]
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: CFD Training Series: Efficient HPC implementation for Lagrangian particle tracking Date: October 26, 2023 Time: 1.00 pm - 5.00 pm Format: hybrid More Information Description Short abstract: In this course, the basics of Lagrangian point particle methods for the application on HPC systems are covered. The course consists of an introduction to […]
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: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation Date: November 02, 2023 Time: 1.00 pm - 5.00 pm Format: online More Information Description Short abstract: In 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. […]
Name: Parallelization in OpenFOAM for HPC Deployment Date: November 06, 2023 & November 16, 2023 Time: 10am - 4pm Format: Online Flipped Classroom focusing on hands-on training More Information Description Short abstract: OpenFOAM is an open source, mature and established C++ library for computational continuum mechanics (CCM) including Computational Fluid Dynamics (CFD). For […]
Name: Introduction to Machine Learning and Deep Learning Date: November 09, 2023 & November 10, 2023 Time: 9 a.m to 1 p.m. Format: Online More Information 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 […]
