Dokument: A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics
| Titel: | A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=72590 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20260316-124421-1 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Luo, Yu [Autor] Liang, Yunfeng [Autor] Reiter, Detlev [Autor] Brezinsek, Sebastijan [Autor] Ren, Pei [Autor] Xu, S. [Autor] Wang, E. [Autor] Cai, J. [Autor] Feng, Y. [Autor] Knieps, A. [Autor] | |||||||
| Dateien: |
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| Stichwörter: | Wendelstein 7-X , machine learning , Bayesian inference , EMC3-EIRENE | |||||||
| Beschreibung: | A neural network-based method is developed to fast optimize EMC3-EIRENE input parameters, enabling EMC3-EIRENE to produce synthetic data that closely match experimental measurements on Wendelstein 7-X. Initially, an EMC3-EIRENE simulation database covering a range of key input parameters is generated. Trained on this database, a feed-forward neural network (FNN) surrogate model efficiently maps EMC3-EIRENE input parameters to synthetic signals corresponding to experimentally observed physical quantities. Subsequently, the trained surrogate model is incorporated into a Bayesian inference framework with Dynamic Nested Sampling to infer posterior distributions of the EMC3-EIRENE input parameters. In this step, the FNN-predicted synthetic data are compared with the experimental data, and the likelihood function explicitly accounts for the measurement uncertainties of the selected diagnostics. EMC3-EIRENE simulations using the maximum a posteriori estimates derived from these posterior distributions reproduce experimental measurements with satisfactory accuracy. This neural network-based method significantly reduces computational costs and the need for manual parameter tuning, and it can be generalized to other similar modeling codes. | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Luo, Y., Xu, S., Liang, Y., Wang, E., Cai, J., Feng, Y., Reiter, D., Knieps, A., Brezinsek, S., Harting, D., Krychowiak, M., Gradic, D., Ren, P., Zhang, D., Gao, Y., Fuchert, G., Pandey, A., & Jakubowski, M. (2025). A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics. Nuclear Fusion, 65(9), Article 096016. https://doi.org/10.1088/1741-4326/adf75f | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät | |||||||
| Dokument erstellt am: | 16.03.2026 | |||||||
| Dateien geändert am: | 16.03.2026 |
