Schlagwort: ‘HPC’
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.
Training: Visualization and Analysis of Atomistic Simulation Data in OVITO
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
Desciption
Post-processing and analysis of atomistic simulations are essential steps to extract knowledge from the computed trajectories. Many commonly used algorithms are already implemented in OVITO and ready to be used on large datasets containing up to billions of atoms.
During day 1 you will learn how to load your simulation outputs into OVITO and process them in the graphical user interface using the built-in algorithms. We will discuss the most important tools and practice using them in hands-on exercises.
On day 2 we will look at the development of custom processing algorithms using OVITO’s Python extension interface, which lets you solve more complex analysis tasks.
On the last day we will step away from the graphical user interface and take a look at automated workflow scripts. This feature of OVITO lets you generate batch analysis pipelines that may be executed on HPC infrastructure to perform computationally intensive data analysis and visualization tasks in a reproducible way.
CFD Training Series: Introduction to Turbulence Modeling and Numerical Implementation
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
Desciption
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.
CFD Training Series: Introduction to Discontinuous Galerkin Methods for Flow Problems
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
Desciption
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.
Training: Introduction to Machine Learning & Deep Learning
Name: Training: Introduction to Machine Learning & Deep Learning
Event type: Workshop
Date: October 19 to 20, 2022
Time: 9am – 1pm (2×4 Hours, 3 hours of presentation, 4 hours of hands-on live exercises, and a discussion)
Format: Online
Target Audience: ML/DL beginners, Scientists who want to apply ML methods
Capacity: Unlimited
Requirements (required knowledge/experience): Laptop and a working internet connection
Contact Person: Zahra Sadeghibogar and Jonas Seng
Desciption
Not only in economics Machine- and Deep-Learning (ML/DL) are inherently used to solve highly complex problems in a datadriven 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.
This workshop will introduce the basics of ML and DL in a theoretical and practical way with the State of Art technologies. The participants will learn how to design ML-models by themselves and will learn about possible pitfalls when applying ML in the real world.
Agenda
Day 1 – October 19, 2022
9:00 – 9:15
Welcome
9:15 – 9:35
Setup
9:35 – 10:00
Data Preparation (Presentation)
10:00 – 10:30
Data Preparation (Hands On)
10:30 – 10:45
Break
10:45 – 11:15
Random Forests (Presentation)
11:15 – 11:45
Random Forests (Hands On)
11:45 – 12:15
Generalized Linear Models (Presentation)
12:15 – 12:45
Generalized Linear Models (Hands On)
12:45 – 13:00
Wrap Up
Day 2 – October 20, 2022
9:00 – 9:15
Welcome
9:15 – 9:45
Neural Networks (Presentation)
9:45 – 10:15
Neural Networks (Hands On)
10:15 – 10:45
Hyperparameter Tuning (Presentation)
10:45 – 11:15
Hyperparameter Tuning (Hands On)
11:15 – 11:30
Break
11:30 – 12:00
Pitfalls of Neural Networks (Presentation)
12:00 – 12:10
Wrap Up
12:10 – 13:00
Discussion & Feedback
Training: Parallelization in OpenFOAM for HPC Deployment
Name: Training: Parallelization in OpenFOAM for HPC Deployment
Event type: Workshop
Date: October 18, 2022
Time: 10am – 4pm
Format: Online
Target Audience: HPC users
Requirements (required knowledge/experience):
The participants are required to have a working operation system (Linux) and the most recent version of OpenFOAM installed. For more details and instructions, please refer to
- https://develop.openfoam.com/Development/openfoam/-/blob/master/doc/Build.md
- https://develop.openfoam.com/Development/openfoam/-/wikis/precompiled/
- https://www.openfoam.com/documentation/system-requirements
To take full advantage of the course offering it would be advisable to have a standing knowledge of using Linux, as it is used for all exercises. Basics in C++ programming will be required for some of the exercises as well as some insights into CFD theory (finite volume method, basic concepts of discretization).
Contact Person: Mohammed Elwardi Fadeli and Dr. Holger Marschall
Desciption
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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