Photo of scientists participating in a training course in HLRS's Ruehle Saal
All communication will be done through Zoom, Slack and email.

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, and NVIDIA for GCS, the Gauss Centre for Supercomputing.


Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

Apr 26, 2023

End date

Apr 27, 2023



Entry level


Course subject areas

Community-Specific Courses


Data in HPC / Deep Learning / Machine Learning


Artificial Intelligence

Big Data

Deep Learning

Machine Learning

Scientific Machine Learning

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Prerequisites and content levels

  • Basic experience with Python. No GPU programming experience is required.
  • Some knowledge in PDE theory (e.g. weak formulation).
Content levels
  • Intermediate level: 3 hours
  • Advanced level: 7 hours

Learn more about course curricula and content levels.


Dr. Niki Andreas Loppi (NVIDIA) - Lead instructor.

Dr. Mozhgan Kabiri Chimeh (NVIDIA).

Dr. Tobias Haas, Dr. Lorenzo Zanon, Dr. Khatuna Kakhiani (HLRS).


(Subject to Change)

All times in CEST

Day 1 - Wednesday, April 26, 2023: 09:00 AM - 12:30 PM

  • 09:00 AM - 09:15 AM: Welcome (Moderator and Host)
  • 09:15 AM - 10:15 AM: Invited Talk by Dr. Alexander Heinlein, Delft University of Technology (TU Delft)
    • Title: Neural networks with physical constraints, domain decomposition-based network architectures, and model order reduction
  • 10:20 AM - 11:20 AM: Invited Talk by Dr. Clement Etienam, NVIDIA
    • Title: An Accelerated Reservoir Inverse Modelling Workflow with NVIDIA’s Modulus
  • 11:20 AM - 11:30 AM: Break
  • 11:30 AM - 12:30 PM: Connecting to a Cluster

Day 2 - Thursday, April 27, 2023: 09:00 AM - 05:30 PM

  • 09:00 AM - 09:05 AM: Welcome (Moderator and Host)
  • 09:05 AM - 09:35 AM: Introduction: Data Driven vs PINN Approach (Lecture)
  • 09:35 AM - 10:05 AM: What is NVIDIA Modulus? (Lecture)
  • 10:05 AM - 11:35 AM: Lab 1: Solving Partial Differential Equations using Modulus
  • 11:35 AM - 12:30 PM: Lab 2: Solving Transient Problems and Inverse Problems using Modulus
  • 12:30 PM - 01:30 PM: Lunch Break
  • 01:30 PM - 02:00 PM: Lab 2 (Continued)
  • 02:00 PM - 05:15 PM: Mini Challenge
  • 05:15 PM - 05:30 PM: Wrap up and Q&A

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 (tba).

Registration information

Register via the button at the top of this page.

This course is offered in cooperation by HLRS, and NVIDIA. Registration is done via hosted by Your registration data will be transferred to these partners. For legal notes see the Privacy Policy.

Registration deadline: 05/04/2023.


This event is free of charge.


Khatuna Kakhiani, phone 0711 685 65796, kakhiani(at)


HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.

This course is not part of the PATC curriculum and is not sponsored by the PATC program.

HLRS is also member of the Baden-Württemberg initiative bwHPC.

Further courses

See the training overview and the Supercomputing Academy pages.

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