https://github.com/digital-porous-media/dpm_fno_workshop
2025 Workshop on Fourier Neural Operators
Science Score: 26.0%
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Low similarity (3.2%) to scientific vocabulary
Repository
2025 Workshop on Fourier Neural Operators
Basic Info
- Host: GitHub
- Owner: digital-porous-media
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 265 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
2025 DPM IAP Workshop on Fourier Neural Operators
This hands-on workshop introduces Fourier Neural Operators (FNOs) — a deep learning approach for approximating solution operators of partial differential equations. Participants will gain both theoretical background and practical experience implementing FNOs in PyTorch, with two example applications: time series prediction of mean curvature flow and steady-state pressure field approximation in Darcy flow. Hands-on portions may be run on Texas Advanced Computing Center clusters to accommodate computational demands.
Prerequisites: Working knowledge of Python, basic familiarity with neural networks and Pytorch, and a foundational understanding of PDEs. Some experience with working on remote clusters is recommended, but not required.
Owner
- Name: Digital Porous Media
- Login: digital-porous-media
- Kind: organization
- Email: bcchang@utexas.edu
- Website: https://www.digitalrocksportal.org/
- Repositories: 1
- Profile: https://github.com/digital-porous-media
GitHub Events
Total
- Watch event: 1
- Public event: 1
- Push event: 7
Last Year
- Watch event: 1
- Public event: 1
- Push event: 7
Dependencies
- h5py *
- lightning *
- matplotlib *
- numpy *
- omegaconf *