iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866913088442204160 |
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| author | Artigues, Victor Merlo, Gabriele Jenko, Frank |
| author_facet | Artigues, Victor Merlo, Gabriele Jenko, Frank |
| contents | We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local nonlinear gyrokinetic model, which allows us to compute gradients of any simulation output with respect to any input via automatic differentiation. We show that even if the stochastic nature of turbulence prevents the exact evaluation of gradients of nonlinear quantities of interest, they can still be successfully used to perform outer-loop tasks, such as profile predictions. This work enables the integration of gyrokinetics into automated parameter optimization, uncertainty quantification, sensitivity analysis, and AI workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03086 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow Artigues, Victor Merlo, Gabriele Jenko, Frank Plasma Physics We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local nonlinear gyrokinetic model, which allows us to compute gradients of any simulation output with respect to any input via automatic differentiation. We show that even if the stochastic nature of turbulence prevents the exact evaluation of gradients of nonlinear quantities of interest, they can still be successfully used to perform outer-loop tasks, such as profile predictions. This work enables the integration of gyrokinetics into automated parameter optimization, uncertainty quantification, sensitivity analysis, and AI workflows. |
| title | iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2605.03086 |