Abstract
STFNO (Sparsified Time-dependent PDEs FNO code) is an extension of the popular Fourier Neural Operator (FNO) architecture to the solution of coupled systems of time-dependent partial differential equations. STFNO leverages the sparsified dependencies on the field quantities based on the semi-discretiezed form of the PDEs, enabling significant reduction in the number of model parameters. STFNO has been extensively tested on two fusion simulation codes, NIMROD and GTC, and can be easily tailored to other systems of PDEs.
- Developers:
-
Rahman, Mustafa [1] ; Liu, Yang [1]
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Release Date:
- 2025-02-04
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 175974
- Site Accession Number:
- 2025-025
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Rahman, Mustafa, and Liu, Yang.
Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0.
Computer Software.
https://github.com/liuyangzhuan/STFNO.
USDOE.
04 Feb. 2025.
Web.
doi:10.11578/dc.20260219.2.
Rahman, Mustafa, & Liu, Yang.
(2025, February 04).
Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0.
[Computer software].
https://github.com/liuyangzhuan/STFNO.
https://doi.org/10.11578/dc.20260219.2.
Rahman, Mustafa, and Liu, Yang.
"Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0." Computer software.
February 04, 2025.
https://github.com/liuyangzhuan/STFNO.
https://doi.org/10.11578/dc.20260219.2.
@misc{
doecode_175974,
title = {Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0},
author = {Rahman, Mustafa and Liu, Yang},
abstractNote = {STFNO (Sparsified Time-dependent PDEs FNO code) is an extension of the popular Fourier Neural Operator (FNO) architecture to the solution of coupled systems of time-dependent partial differential equations. STFNO leverages the sparsified dependencies on the field quantities based on the semi-discretiezed form of the PDEs, enabling significant reduction in the number of model parameters. STFNO has been extensively tested on two fusion simulation codes, NIMROD and GTC, and can be easily tailored to other systems of PDEs.},
doi = {10.11578/dc.20260219.2},
url = {https://doi.org/10.11578/dc.20260219.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20260219.2}},
year = {2025},
month = {feb}
}