
The Digital Twin Lab at Texas A&M Institute of Data Science will host a collaborative workshop on Reduced Order Modeling (ROM), organized in partnership with Texas A&M High Performance Research Computing and the libROM team at Lawrence Livermore National Laboratory (LLNL). This hands-on workshop is designed for researchers and students keen on exploring the realms of ROM and its applications.
Organizers: Suparno Bhattacharyya, Pravija Danda, Eduardo Gildin, Jean Ragusa, and Jian Tao from Texas A&M University with help from the libROM team.
Location: Blocker 220 & Online (Zoom link will be sent to registered attendees)
Date & Time: April 18th, 1:00 pm – 5:00 pm
We invite participants from diverse backgrounds who are interested in enhancing their understanding and skills in ROM. When registering, please share your background and motivation for attending the workshop. This information will help us tailor the workshop to meet your needs better.
Workshop Resources:
Please refer to the following link for access to our workshop resources: pylibROM Workshop Repo, where you’ll find materials such as slides, Jupyter Notebooks, and other instructions related to the workshop.
Recorded Session (Slides):
Schedule
1:00PM Session 1: Introduction to Reduced Order Modeling and pylibROM
- Time: 1 hour
- Topics Covered:
- Overview of ROM and its significance in simulation and data analysis.
- Introduction to pylibROM and its capabilities.
- Real-world applications of ROM.
- Activities: Presentation on ROM basics followed by an interactive Q&A session.
2:00PM Session 2: Setting Up the Environment
- Duration: 15 minutes (Flexible)
- Topics Covered:
- Required software and dependencies for working with pylibROM.
- A step-by-step guide to installing the pylibROM package.
- Activities: Hands-on installation tutorial and troubleshooting common issues.
2:15PM Session 3: Basic Concepts and Operations in pylibROM
- Duration: 1 hour
- Topics Covered:
- Understanding pylibROM architecture and basic functions.
- Hands-on exercise with ROM example (Poisson equation).
- Activities: Code walkthrough and hands-on exercises for better understanding.
Break: 15 minutes
3:30PM Session 4: Advanced Features of pylibROM
- Duration: 1 hour
- Topics Covered:
- Deep dive into dynamic mode decomposition (DMD).
- Data compression techniques and applications.
- Practical exercises with advanced DMD examples in pylibROM.
- Activities: Group exercises, in-depth discussions, and advanced code examples.
4:30PM Wrapup
Additional Resources and Post-Workshop Support
Participants can get access to LLNL’s GitHub repository for pylibROM: GitHub – LLNL/pylibROM. Additionally, we will establish a forum or group for ongoing support and discussions among participants, fostering a community of practice around ROM and pylibROM and their applications at Texas A&M University. More information about Texas A&M High Performance Research Computing can be found at https://hprc.tamu.edu/.
