Novel aggregate machine learning and transport modeling profile predictions

Progress is presented on a first-of-its-kind "aggregate" model that forecasts the state of a tokamak ~an energy confinement time into the future, to be used in tokamak control and as a "black-box" representation of devices for offline analysis. This aggregate model has been trained on DIII-D+ASDEX experimental data alongside outputs from TRANSP and ASTRA predictive transport simulations. The result is a model more portable and adaptable (i.e. applicable to new regimes and devices) than ML or empirical models trained exclusively on experimental data, yet more accurate than transport simulations. We show the aggregate model's superior performance over our previous fully data-driven forecaster for predicting unseen regimes. Results from a June 2021 DIII-D experiment implementing a similar neural network forecaster for model-predictive control (of temperature and pressure profiles via neutral beam power) are also presented. We discuss how our new "aggregate" methodology can be incorporated into the realtime DIII-D algorithm for a follow-up model-predictive control experiment in the 2022 campaign.