BOOTCAMP: AI for Science Bootcamp

This Bootcamp will be held online with Zoom. Cluster dry run already on Mon, June 24.

This bootcamp focuses on Scientific Machine Learning (differently to the 2023 edition).

Deep Learning (DL) has revolutionized the way of performing classification, pattern recognition, and regression tasks in various application areas. Scientific applications solving linear and non-linear equations with demanding accuracy and computational performance requirements can use a class of DL networks, called Physics-Informed Neural Networks (PINN). In fact, PINNs are specifically designed to integrate scientific computing equations, such as Ordinary Differential Equations (ODE), Partial Differential Equations (PDE), non-linear, and integral-differential equations into the DL network training.

This workshop introduces Scientific Machine Learning (SciML) with PINN and provides hands-on experience with the PDE solver NVIDIA Modulus, a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. This online Bootcamp is a hands-on learning experience where you will be guided through step-by-step instructions with teaching assistants on hand to help throughout.

The Bootcamp is co-organised by HLRS, JSC, LRZ, VSC Vienna, UDG, RISE, LiU, OpenACC.org and NVIDIA for EuroCC Austria, EuroCC@GCS, EuroCC Montenegro, and EuroCC Sweden, all National Competence Centres for High-Performance Computing.

Location

Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

Jun 25, 2024
09:00

End date

Jun 26, 2024
13:30

Language

English

Entry level

Intermediate

Course subject areas

Community-Specific Courses

Simulation

Data in HPC / Deep Learning / Machine Learning

Topics

Artificial Intelligence

Big Data

Deep Learning

Machine Learning

Scientific Machine Learning

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Prerequisites and content levels

Prerequisites
  • Basic experience with Python. No GPU programming experience is required.
  • Some knowledge in PDE theory (e.g. weak formulation).
Content levels
  • Intermediate level: 7 hours 15 mins

Learn more about course curricula and content levels.

Instructors

Main lecturer: Miguel Martinez (NVIDIA)

Teaching Assistants:

  • Troy Crutcher (NVIDIA)
  • Claudia Blaas-Schenner, Moritz Siegel. Siegfried Höfinger, Soner Steiner, Ivan Vialov (VSC Vienna)
  • Layal Ali, Lorenzo Zanon, Khatuna Kakhiani, Tobias Haas, Maksym Deliyergiyev (HLRS)
  • Anoop Chandran, Oleg Filatov (JSC)
  • Ajay Navilarekal, Darshan Thummar, Maja Piskac, Navdar Karabulut (LRZ)
  • Dejan Babic, Ivan Jovovic (UDG)
  • Martin Simonsson (ENCCS/RISE), Xuan Gu (LiU)

 

Agenda

CEST time:

Day 0 (Mon, June 24): Cluster Dry Run

  • 11:30  - 12:00 Cluster Dry Run Session

Day 1 (Tue, June 25): Introduction

  • 09:00 - 09:15 Welcome (Moderator)
  • 09:15 - 09:30 Connecting to a cluster
  • 09:30 - 10:00 Introduction to NVIDIA Modulus (Lecture)
  • 10:00 - 12:00 Physics-Informed approach to an AI for Scientific application (Lecture and Lab)
    • Lab 1: Simulating Projectile Motion
    • Lab 2: Steady State Diffusion in a Composite Bar using PINNs
  • 12:00 - 12:30 Wrap up and Q&A

Day 2 (Wed, June 26): Advanced Topics

  • 09:30 - 10:30 Physics-Informed approach to an AI for Scientific application (Lecture and Lab)
    • Lab 3: Forecasting weather using Navier-Stokes PDE
    • Lab 4: Spring mass problem - Solving transient problems and inverse problems - Optional
  • 10:30 - 12:15 Data-driven approach to an AI for Scientific application. (Lecture and Lab)
    • Lab 1: Solving the Darcy-Flow problem using FNO
    • Lab 2: Solving the Darcy-Flow problem using AFNO
    • Lab 3: Forecasting weather using FourCastNet
  • 12:15 - 12:30 Wrap up and QA

Hands-on sessions

Attendees will be given access to a GPU cluster for the duration of the Bootcamp.

The code is publicly available on github (End-to-End-AI-for-Science).

Registration information

Please register at OpenACC-Standard.org via the button at the top of this page with your institutional e-mail address to prove your affiliation.

The final participants will be selected and informed after the registration deadline has passed.

This course is offered in cooperation by HLRS, JSC, LRZ, VSC Vienna, UDG, RISE, LiU, OpenACC.org and NVIDIA. Registration is done via www.gpuhackathons.org hosted by OpenACC-Standard.org. Your registration data will be transferred to these partners. For legal notes see the Privacy Policy.

Registration closes on May 14, 2024 at 23:55.

Fees

  • Students without Master's degree or equivalent. Participants from EU or EuroCC countries only: 0 EUR
  • PhD students or employees at a German university or public research institute: 0 EUR
  • PhD students or employees at a university or public research institute in an EU or EuroCC country other than Germany: 0 EUR.
  • Other participants, e.g., from industry, other public service providers, or government. Participants from EU or EuroCC countries only: 0 EUR

Our course fee includes coffee breaks (in classroom courses only).

For lists of EU and EuroCC countries have a look at the Horizon Europe and EuroCC website.

Only participants from institutions belonging to these countries can take part in this course.

Contact

Maksym Deliyergiyev phone 0711 685 87261, maksym.deliyergiyev(at)hlrs.de

HLRS Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.

This course is provided within the framework of EuroCC2.

Official course URL(s)

http://www.hlrs.de/training/2024/BC-AI-NV and course website at Open Hackathons.

Further courses

See the training overview and the Supercomputing Academy pages.

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