Schlagwort: ‘HPC’
NHR4CES: CFD Training Series: Introduction to Kernel-based approximation methods with applications to fluid dynamics
Name: CFD Training Series: Introduction to Kernel-based approximation methods with applications to fluid dynamics
Date: November 23, 2023
Time: 1.00 pm – 5.00 pm
Format: hybrid
Description
Short abstract:
When data is provided in an unstructured format or is high-dimensional, classical interpolation or approximation schemes as Finite-Element Methods (FEM) struggle to be accurate and efficient. An alternative is provided by kernel-based approaches in the Reproducing Kernel Hilbert space (RKHS) framework. Common applications range from support vector machines in context of machine learning to reconstruction of image data. In this course, we will introduce the framework for kernel-based approximation schemes and discuss how one can implement them efficiently. At the end we will look at possible applications of kernel-based methods in context of fluid dynamics and particle methods.
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NHR4CES: Data-Driven Healthcare: Leveraging Visualization and Data Management for Digital Patient Studies
Name: Data-Driven Healthcare: Leveraging Visualization and Data Management for Digital Patient Studies
Date: November 21, 2023 & November 22, 2023
Time: 1.00 pm – 5.00 pm
Format: Online
Description
Short abstract:
In today’s rapidly advancing medical landscape, data has become an invaluable asset, empowering healthcare professionals and researchers to make informed decisions, derive insights, and develop innovative solutions. However, coping with the sheer volume and complexity of data while ensuring security and privacy aspects is a daunting task. To harness the full potential of this data-rich environment, effective visualization techniques and robust data management strategies have emerged as indispensable tools.
This NHR4CES workshop seeks to shed light on how professionals and researchers can effectively leverage visualization and data management to tackle current and emerging challenges, supporting medical experts in their daily work, research, and improving communication on and with patients. Researchers and users from different domains are invited to exchange and discuss ideas alongside a program filled with presentations by professionals of different fields as well as panel discussion.
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NHR4CES: CFD Training Series: Introduction to Discontinuous Galerkin Methods for Flow Problems
Name: CFD Training Series: Introduction to Discontinuous Galerkin Methods for Flow Problems
Date: November 16, 2023
Time: 1.00 pm – 5.00 pm
Format: Online
Description
Short abstract:
In this course, we cover the main building blocks to solve fluid flow problems using the Discontinuous Galerkin (DG) method. The course consists of a combination of presentations and hands-on exercises in which a simple DG flow solver is implemented and run on some test cases within our open-source code framework BoSSS.
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NHR4CES: Parallelization in OpenFOAM for HPC Deployment
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
Description
Short abstract:
OpenFOAM is an open source, mature and established C++ library for computational continuum mechanics (CCM) including Computational Fluid Dynamics (CFD). For leveraging its full potential, it is crucial to efficiently use the high-performance computing (HPC) resources on modern distributed-memory parallel computer architectures. This must be based on a sound understanding of parallelization in OpenFOAM and HPC techniques available.
The training will be concerned with introducing the participants to the different concepts of parallelization, along with code examples for illustration. Moreover, we will provide hands-on exercises to further deepen and solidify the transferred knowledge. The participants will further gain an overview over the distinct techniques and dedicated tools involved to run a massively parallel computation using OpenFOAM, as well as over ongoing HPC-related activities in research and development.
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NHR4CES: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation
Name: CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation
Date: November 02, 2023
Time: 1.00 pm – 5.00 pm
Format: online
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. 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.
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NHR4CES: CFD Training Series: Efficient HPC implementation for Lagrangian particle tracking
Name: CFD Training Series: Efficient HPC implementation for Lagrangian particle tracking
Date: October 26, 2023
Time: 1.00 pm – 5.00 pm
Format: hybrid
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 the applied method, followed by a hands-on exercise using the in-house simulation framework m-AIA. The topics covered are spherical and non-spherical particles and the efficient implementation of point particle methods for the use in HPC. The course will be held in person at the Chair of Fluid Mechanics and Institute of Aerodynamics at RWTH Aachen University in Aachen. The presentations will also be streamed online.
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NHR4CES: Introduction to Machine Learning and Deep Learning
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
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.
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NHR4CES: Process Mining and Scientific Workflows running on the HPC cluster
Name: Process Mining and Scientific Workflows running on the HPC cluster
Date: December 11, 2023
Time: 9 a.m to 1 p.m.
Format: Online
Description
Short abstract: The goal of Process Mining is to turn event data into insights and actions. On the other side, there exist scientific workflows running on HPC clusters. But, why do we need process mining to analyze scientific workflows running on HPC clusters? For two reasons, documentation of scientific workflows and detection of bottlenecks that slow down the execution of scientific workflows. That is why we are doing to implement a cockpit to monitor HPC processes with Process Mining techniques. Another perspective is supporting Process Mining workflows for scientific experiments to facilitate the use and also improve the performance of Process Mining techniques.
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NHR4CES: Machine Learning in Combustion
Name: Machine Learning in Combustion
Date: November 09 & 10, 2023
Time: 2 pm – 6 pm
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 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.
This 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.
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NHR4CES Community Workshop 2023
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
Desciption
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 Computational Fluid Dynamics (CFD).
The combination of CFD with ML has been already applied to several 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.
The workshop is designed to highlight some of the areas of the highest potential impact, including improving turbulence and combustion closure modeling, developing reduced-order models, and designing versatile neural network architectures. Emerging ML areas that are promising for CFD, as well as some potential limitations, will be discussed.
The workshop aims at gathering different research groups, by providing a venue to exchange new ideas, discuss challenges, and expose this new research field to a broader community.
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