aiqbench analysis
This study applies Physics-Informed Neural Networks (PINNs) to solve an inverse problem in stellarator fusion research, inferring heat conductivity from plasma profile measurements while strictly adhering to underlying transport physics. By simultaneously training networks to reconstruct density and temperature profiles alongside a third network for effective conductivity, the framework achieves high accuracy with errors below 10% in data-constrained regions. The work demonstrates a practical trade-off between reconstruction fidelity and data availability, validated against both synthetic benchmarks and experimental datasets from the TJ-II device.
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 method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $\kappa_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.
Authors:J. Gallego (1), P. Protopapas (2), A. Bustos (1), A. Alonso (3), S. Barquero (3), A. Baciero (3), I. Rivera (3), J. A. Moríñigo (1), R. Mayo-García (1) ((1) Departamento de Tecnología, CIEMAT, Spain, (2) Harvard John A. Paulson School of Engineering and Applied Sciences, USA, (3) Laboratorio Nacional de Fusión, CIEMAT, Spain)
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 method combines radial profile measurements of electron density and temperature with the residual of a reduced one-dimensional SOL transport equation, so that the inferred conductivity is constrained by both the measurements and the underlying transport model. Three neural networks are trained simultaneously: two reconstruct the temperature and density profiles as functions of the radial coordinate and transported power, while a third represents the effective conductivity as a function of the local density and temperature. The framework is first validated using synthetic data generated from a prescribed conductivity function, allowing the inferred $\kappa_\perp(n,T)$ to be compared directly with the ground truth. The model recovers the imposed functional dependence with errors below $10~\%$ in the data-constrained region. Bootstrap resampling is shown to provide a practical indicator of prediction reliability and consistency. A scan in the number of plasma profiles used for training and the number of radial measurement positions per profile identifies a practical trade-off between reconstruction accuracy and data availability. Finally, the method is applied to an experimental dataset from the TJ-II stellarator obtained with the helium-beam diagnostic. This exploratory application provides an initial estimate of the effective SOL conductivity and illustrates the potential of inverse PINNs for extracting transport information from plasma edge measurements.
Plasma Physics (physics.plasm-ph); Artificial Intelligence (cs.AI)
arXiv:2609.11628 [physics.plasm-ph]
(or arXiv:2609.11628v1 [physics.plasm-ph] for this version)
https://doi.org/10.48550/arXiv.2609.11628 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 309 words
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