Barrier distribution extraction via Gaussian process regression
This work presents a novel method for extracting potential barrier distributions from experimental fusion cross sections. We utilize a simple Gaussian process regression (GPR) framework to model the observed cross sections as a function of energy for three nuclear systems. The GPR approach offers a...
Saved in:
| Main Author: | Godbey Kyle |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
|
| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2024/16/epjconf_fusion2024_01001.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Curb Detection and Mapping via Robust Iterative Gaussian Process Regression
by: Di Wang, et al.
Published: (2024-06-01) -
Gaussian Process Regression with Soft Equality Constraints
by: Didem Kochan, et al.
Published: (2025-01-01) -
Error Quantification of Gaussian Process Regression for Extracting Eulerian Velocity Fields from Ocean Drifters
by: Junfei Xia, et al.
Published: (2025-02-01) -
A Novel Cost-Effective and Distributed in-Band OSNR Monitoring Method Using Gaussian Process Regression
by: Chunjie Hu, et al.
Published: (2019-01-01) -
Improving soil moisture prediction using Gaussian process regression
by: Xiaomo Zhang, et al.
Published: (2025-08-01)