Application of interpretable machine learning for cross-diagnostic inference on the ST40 spherical tokamak
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914888667889664 |
|---|---|
| author | Pyragius, Tadas Colgan, Cary Lowe, Hazel Janky, Filip Fontana, Matteo Cai, Yichen Naylor, Graham |
| author_facet | Pyragius, Tadas Colgan, Cary Lowe, Hazel Janky, Filip Fontana, Matteo Cai, Yichen Naylor, Graham |
| contents | Machine learning models are exceptionally effective in capturing complex non-linear relationships of high-dimensional datasets and making accurate predictions. However, their intrinsic ``black-box'' nature makes it difficult to interpret them or guarantee ``safe behavior'' when deployed in high-risk applications such as feedback control, healthcare and finance. This drawback acts as a significant barrier to their wider application across many scientific and industrial domains where the interpretability of the model predictions is as important as accuracy. Leveraging the latest developments in interpretable machine learning, we develop a method to parameterise ``black-box'' models, effectively transforming them into ``grey-box'' models. We apply this approach to plasma diagnostics by creating a parameterised synthetic Soft X-Ray imaging $-$ Thomson Scattering diagnostic, which predicts high temporal resolution electron temperature and density profiles from the measured soft X-ray emission. The ``grey-box'' model predictions are benchmarked against the trained ``black-box'' models as well as a diverse range of plasma conditions. Our model-agnostic approach can be applied to various machine learning architectures, enabling direct comparisons of model interpretations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of interpretable machine learning for cross-diagnostic inference on the ST40 spherical tokamak Pyragius, Tadas Colgan, Cary Lowe, Hazel Janky, Filip Fontana, Matteo Cai, Yichen Naylor, Graham Plasma Physics Applied Physics Instrumentation and Detectors Machine learning models are exceptionally effective in capturing complex non-linear relationships of high-dimensional datasets and making accurate predictions. However, their intrinsic ``black-box'' nature makes it difficult to interpret them or guarantee ``safe behavior'' when deployed in high-risk applications such as feedback control, healthcare and finance. This drawback acts as a significant barrier to their wider application across many scientific and industrial domains where the interpretability of the model predictions is as important as accuracy. Leveraging the latest developments in interpretable machine learning, we develop a method to parameterise ``black-box'' models, effectively transforming them into ``grey-box'' models. We apply this approach to plasma diagnostics by creating a parameterised synthetic Soft X-Ray imaging $-$ Thomson Scattering diagnostic, which predicts high temporal resolution electron temperature and density profiles from the measured soft X-ray emission. The ``grey-box'' model predictions are benchmarked against the trained ``black-box'' models as well as a diverse range of plasma conditions. Our model-agnostic approach can be applied to various machine learning architectures, enabling direct comparisons of model interpretations. |
| title | Application of interpretable machine learning for cross-diagnostic inference on the ST40 spherical tokamak |
| topic | Plasma Physics Applied Physics Instrumentation and Detectors |
| url | https://arxiv.org/abs/2407.18741 |