Roadmap: Scientific Machine Learning (SciML)
This roadmap covers the fundamentals of Scientific Machine Learning, combining traditional scientific computing with modern machine learning techniques.
The topics in this roadmap include materials that are collected together in Jupyter notebooks that can be run to reproduce the results contained in each topic page. Access to the notebooks is described in the Lab page at the end of each topic. If you would like to run the code in the notebook as you work through the materials in this topic, consult the Lab pages for information on how to proceed.
These roadmap materials are made available through Chishiki-AI, a transformative project at the forefront of integrating Artificial Intelligence (AI) with Civil and Environmental Engineering (CEE). Chishiki.AI, meaning 'knowledge through AI,' is a pioneering initiative funded by the National Science Foundation and led by a team of experts at the University of Texas at Austin in collaboration with Cornell University.
Chishiki-AI is funded by the National Science Foundation, award #2321040.
Objectives
The objectives for the topics in SciML are:
- Establish the SciML landscape and understand Automatic Differentiation (AD) as the core engine for most SciML methods.
- Dive deep into using PINNs for solving forward and inverse problems involving differential equations.
- Understand methods for learning mappings between function spaces to accelerate solutions for entire families of PDEs.
- Learn to apply GNNs as powerful surrogates for physical systems represented by graphs or meshes.
Prerequisites
The prerequisites for the topics in SciML are:
- Experience with programming in Python.
- Some knowledge of physics.
- Some knowledge of differential equations.
Requirements
To perform the exercises outlined in this module you will need an allocation on TACC resources with access to the TACC Analysis Portal.
If you don't have an allocation: Register with the Chishiki-AI project to be added to the TACC account.