TGLF-WINN: data-efficient deep learning surrogate for turbulent transport modeling in fusion

The trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations remain computationally expensive. Neural network (NN) surrogates offer accelerated inference with fully differentiable ap...

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Detalles Bibliográficos

Principais autores:	Yadi Cao, Futian Zhang, Wesley Liu, Tom Neiser, Orso Meneghini, Lawson Fuller, Sterling Smith, Raffi Nazikian, Brian Sammuli, Rose Yu
Formato:	Artigo
Idioma:	Inglês
Publicado:	IOP Publishing 2026-01-01
Series:	Nuclear Fusion
Assuntos:	fusion core turbulent transport machine learning active learning
Acceso en liña:	https://doi.org/10.1088/1741-4326/ae72a6
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