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Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X

Title: Neural network-based surrogate model for 3D edge-plasma transport in the standard configuration of W7-X
Authors: W7-X Team; Luo, Y.; Xu, S.; Liang, Y.; Wang, E.; Cai, J.; Knieps, A.; Pei, R.; Tan, Muzhi; Feng, Y.; Reiter, D.; Brezinsek, S.; Harting, D.; Krychowiak, M.; Gradic, D.; Jakubowski, M.
Source: Nuclear Fusion, 66 (1), 016038 ; ISSN: 0029-5515, 1741-4326
Publisher Information: International Atomic Energy Agency
Publication Year: 2025
Collection: KITopen (Karlsruhe Institute of Technologie)
Subject Terms: ddc:600; Technology; info:eu-repo/classification/ddc/600
Description: 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.
Document Type: article in journal/newspaper
Language: English
ISSN: 0029-5515; 1741-4326
Relation: info:eu-repo/grantAgreement/EC/EURATOM/101052200/EU//EUROfusion; info:eu-repo/semantics/altIdentifier/wos/001625806500001; info:eu-repo/semantics/altIdentifier/issn/0029-5515; info:eu-repo/semantics/altIdentifier/issn/1741-4326; https://publikationen.bibliothek.kit.edu/1000188250
Availability: https://publikationen.bibliothek.kit.edu/1000188250
Rights: info:eu-repo/semantics/openAccess
Accession Number: edsbas.C61AE881
Database: BASE