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Physics-Informed Neural Networks Infer Plasma Conductivity in Stellarators

Researchers have developed a novel framework using Physics-Informed Neural Networks (PINNs) to infer the perpendicular energy conductivity in the scrape-off layer of stellarator devices. This method combines plasma profile measurements with a reduced transport equation to constrain the inferred conductivity, $\kappa_\perp(n,T)$. The framework was validated with synthetic data, achieving less than 10% error, and has been applied to experimental data from the TJ-II stellarator, providing an initial estimate of the effective SOL conductivity. AI

IMPACT This research demonstrates a novel application of AI in inferring complex physical properties, potentially advancing plasma physics research and fusion energy development.

RANK_REASON The cluster contains a research paper detailing a new methodology using neural networks for plasma physics research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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Physics-Informed Neural Networks Infer Plasma Conductivity in Stellarators

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The cluster contains a research paper detailing a new methodology using neural networks for plasma physics research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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, CIEMA… ·

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

    arXiv:2609.11628v1 Announce Type: cross Abstract: In this work, we develop an inverse Physics-Informed Neural Network (PINN) framework to infer the dependence of the scrape-off layer (SOL) perpendicular heat conductivity on plasma density and temperature, $\kappa_\perp(n,T)$. The…