Dokument: Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X
| Titel: | Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=72817 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20260402-115334-9 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Luo, Yu [Autor] Liang, Yunfeng [Autor] Pei, Ren [Autor] Tan, Muzhi [Autor] Reiter, Detlev [Autor] Brezinsek, Sebastijan [Autor] Xu, S. [Autor] Wang, E. [Autor] Cai, J. [Autor] Knieps, A. [Autor] | |||||||
| Dateien: |
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| Stichwörter: | machine learning , EMC3-EIRENE , edge plasma transport , Wendelstein 7-X | |||||||
| Beschreibung: | This paper presents a neural-network surrogate model for Wendelstein 7-X (W7-X) edge transport simulations, trained on an EMC3-EIRENE dataset that spans nearly the entire operating-parameter space currently explored in the W7-X standard configuration. The model uses an autoencoder to compress EMC3-EIRENE outputs into a low-dimensional latent space representation, then a neural regressor maps EMC3-EIRENE input parameters to those latent vectors, and finally both components are fine-tuned together to predict outputs directly from inputs. In benchmark tests, the improved surrogate outperforms a traditional multilayer perceptron model, most notably in predicting detached regime. Leave-one-value-out evaluation indicates high accuracy within the interpolation domain, with minor degradation when extrapolating. Relative to full EMC3-EIRENE runs, the surrogate provides over a 108 - fold speedup, enabling large-scale parameter scans or real-time feedback control based on 3D transport simulations to become feasible in the future. | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Luo, Y., Xu, S., Liang, Y., Wang, E., Cai, J., Knieps, A., Ren, P., Tan, M., Feng, Y., Reiter, D., Brezinsek, S., Harting, D., Krychowiak, M., Gradic, D., & Jakubowski, M. (2025). Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X. Nuclear Fusion, 66(1), Article 016038. https://doi.org/10.1088/1741-4326/ae203d | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät | |||||||
| Dokument erstellt am: | 02.04.2026 | |||||||
| Dateien geändert am: | 02.04.2026 |
