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arXiv AI By J. Gallego (Departamento de Tecnolog\'ia, CIEMAT, Spain), P. Protopapas (Harvard John A. Paulson School of Engineering and Applied Sciences, USA), A. Bustos (Departamento de Tecnolog\'ia, CIEMAT, Spain), A. Alonso (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), S. Barquero (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), A. Baciero (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), I. Rivera (Laboratorio Nacional de Fusi\'on, CIEMAT, Spain), J. A. Mor\'i\~nigo (Departamento de Tecnolog\'ia, CIEMAT, Spain), R. Mayo-Garc\'ia (Departamento de Tecnolog\'ia, CIEMAT, Spain)

Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices

Read the original on arXiv AI →

The paper presents an inverse Physics-Informed Neural Network (PINN) framework that infers the scrape‑off layer (SOL) perpendicular heat conductivity κ_π(n,T) from radial electron density and temperature measurements combined with a reduced one‑dimensional transport equation. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles, while the third models the conductivity as a function of local density and temperature. The method is validated on synthetic data, achieving errors below 10 % in the data‑constrained region, and is then applied to experimental data from the TJ‑II stellarator using a helium‑beam diagnostic to estimate the effective SOL conductivity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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