Session Info
- Place: Blocker 220, Texas A&M University (Map)
- Time: 10:00 am - 12:00 pm. October 25, 2022
(Latest information is on TAMIDS page)
Presenter
- Steven Shao-Ting Chiu — Ph.D. Student, Department of Electrical Engineering, Texas A&M University
- Advisor: Dr. Ulisses Braga-Neto
Background and Objectives
Julia (https://julialang.org/) is a generic programming language designed for high-performance computing. It solves the “two language problem” that typically occurs in scientific computing. Julia is dynamically typed like scripting language such as Python and can be compiled into native machine code. Besides, composablility via multiple dispatches makes Julia works well on the integration across packages. SciML (https://sciml.ai/) is an open-source software for scientific machine learning based on the Julia language that combines machine learning and scientific computing by integrating numerous standalone packages. Notebly, Julia is an open-source project under an MIT license.
This worhshop aims to introduce the potential of the Scientific Machine Learning field with Julia programming language. First, we will give an introductory overview of the Julia programming language and explore the Julia SciML ecosystem as an example of its application.
Both sessions will include presentations and hands-on sessions. Prior knowledge of Python is recommended, and participants are encouraged to bring their own laptops.
Schedule
| Time | Content |
|---|---|
| 25 min | Introduction to the Julia Programming Language |
| 25 min | The Julia SciML Ecosystem |
| 10 min | Break |
| 30 min | Hands-on session with Julia basics |
| 30 min | Hands-on session with SciML application |
Access to this webpage
https://stevengogogo.github.io/Julia-for-SciMLHands-on session
