Investigating pedestal dependencies at JET using an interpretable neural network architecture
We present NeuralBranch, an interpretable neural network framework. In this work, we use it specifically to predict the pedestal from key engineering parameters in tokamak fusion experiments. The main goal is to uncover intricate relationships that traditional power scalings, with their limited expr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Nuclear Fusion |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1741-4326/adcbc2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|