In case anyone is interested, here is what I have been doing for the past couple months. Link to the slides because my head cuts off some of the graphs: https://docs.google.com/presentation/d/1KKWV9Baf6ujJJOqK9JHD7bsuOgANoR-ZpH1W0nFDbxg/edit?usp=sharing Link to abstract: https://meetings.aps.org/Meeting/DPP20/Session/ZP08.11 Abstract: An experimentally trained saturation rule for the quasilinear TGLF turbulent transport model has been obtained. The wavenumber (k) spectrum of the rule is prescribed as a + b log (k) / k^c, and the coefficients a,b,c are the output of a neural network trained to produce fluxes similar to experimentally inferred fluxes for the nominal parameters of a database of DIII-D discharges. Different neural network architectures and hyperparameters were tested, including reducing the coefficients produced by the model from 6 (having a separate saturation rule per unstable mode) to 3 (one rule for all modes). Using symbolic regression through genetic algorithms, analytic expressions were obtained to map the relationships between a,b,c and input parameters. The correlations of a with collisionality and c with electron temperature gradient scale length are particularly strong. Other forms of the saturation rule wavenumber spectrum prescription are explored. Get 60 Free Days of Unlimited Access to Scribd! https://www.scribd.com/g/6lchwh Follow Me: Twitter: https://twitter.com/khanradcoder Instagram: https://instagram.com/thekhanradcoder/ GitHub: https://github.com/KhanradCoder