Dataset Open Access
VIPR Reflectometry Model: Neural Spline Flow for Neutron Reflectometry Inference (MARIA Dataset)
Rustamov, Jeyhun
This dataset provides a pre-trained Neural Spline Flow (NSF) model for probabilistic inference in neutron reflectometry, developed within the VIPR framework.
The model estimates posterior distributions of thin-film parameters (e.g., thickness, SLD, roughness) directly from reflectivity curves, enabling fast uncertainty quantification and detection of multimodal solutions. It is configured for data from the MARIA reflectometer and corresponds to the probabilistic workflow presented in the associated VIPR publication .
Contents: model weights and configuration file.
Usage: designed for execution within VIPR via YAML-defined inference workflows.
Files
(47.1 MB)
| Name | Size | |
|---|---|---|
|
config.yaml
md5:acbe184714e390b2d0163d9906d3334a |
2.4 kB | Download |
|
model.pt
md5:793137e46081d65d202ece92242c558a |
47.1 MB | Download |
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| Views | 78 | 78 |
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| Data volume | 471.1 MB | 471.1 MB |
| Unique views | 76 | 76 |
| Unique downloads | 16 | 16 |
- Publication date:
- February 11, 2026
- DOI:
-
- Related identifiers:
- Identical to:
https://www.hzdr.de/publications/Publ-43325 - Part of:
10.57967/hf/8498 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International