iGENE: A differentiable flux-tube gyrokinetic code in TensorFlow

We present iGENE, a fully-differentiable TensorFlow implementation of the electromagnetic local non-linear 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 non-linear 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 artificial intelligence workflows.