hannnzh / end-to-end-ai-for-science

This repository containts materials for End to End AI for Science

End-to-End AI for Science

The End-to-End AI for Science Bootcamp provides a step-by-step overview of the fundamentals of deep neural networks, walks attendees through the hands-on experience of building and improving deep learning models using a framework that uses the fundamental laws of physics to model the behavior of complex systems, and enables attendees to visualize the physically accurate outputs of the trained model in near real-time.

Bootcamp contents:

The content is structured in multiple modules covering the following:

• Introduction to NVIDIA Modulus
• Module 1: Physics Informed approaches to an AI for Scientific application.
  • Lab 1: Simulating Projectile Motion
  • Lab 2: Steady State Diffusion in a Composite Bar using PINNs
  • Lab 3: Forecasting weather using Navier-Stokes PDE
  • Lab 4: Spring mass problem - Solving transient problems and inverse problems - Optional
• Module 2: Data-driven approach to an AI for Scientific application.
  • Lab 1: Solving the Darcy-Flow problem using FNO
  • Lab 2: Solving the Darcy-Flow problem using AFNO
  • Lab 3: Forecasting weather using FourCastNet
• Module 3: Visualising Scientific data
  • Lab 1: Introduction to NVIDIA Omniverse
  • Lab 2: Physics-Informed data visualization using Omniverse extensions
  • Lab 3: FourCastNet data visualization using Omniverse extensions

Tools and frameworks:

The tools and frameworks used in the bootcamp are as follows:

• NVIDIA® Modulus
• NVIDIA Omniverse™

Bootcamp duration:

The overall bootcamp will take approximately 14 hours.

Bootcamp prerequisites:

Mathematical background in Differential equations, Python proficiency, and familiarity with deep learning fundamentals and frameworks are required.

Deploying the Bootcamp materials:

For deploying the materials, please refer to the Deployment guide present here

Attribution

This material originates from the OpenHackathons Github repository. Check out additional materials here

Don't forget to check out additional Open Hackathons Resources and join our OpenACC and Hackathons Slack Channel to share your experience and get more help from the community.

Licensing

Copyright © 2023 OpenACC-Standard.org. This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0). These materials may include references to hardware and software developed by other entities; all applicable licensing and copyrights apply.