Results and Lessons Learned from the "Accelerating Radio Frequency Modeling Using Machine Learning" Project

Our SciDAC machine learning (ML) project focused on accelerating radio frequency (RF) computational models to enable real time feedback control such as needed in advanced tokamak (AT) scenarios. Lower hybrid and high harmonic fast fasts are two types of waves used for current profile control in tokamaks. The former typically uses geometric options (ray tracing) coupled with a Fokker-Planck code to simulate the current deposition profile. The later with its longer wavelengths uses physical options (full wave) also coupled with Fokker-Planck. This project created surrogate models with machine learning to reduce simulation time to the order of milli-seconds (ms) enabling real time control and integrated predictive models. Models are capable of predicting heating and current drive profiles (the forward problem), determining wave parameters for a desired deposition profile (the inverse problem), and mapping diagnostic measurements to core deposition profiles (the lateral problem). Training data was generate from simulation parameters in a hypercube sampling method. Three classes of ML models (Random Forest Regression, Gaussian Process Regression, and Multi-Layer Perceptron) trained on the databases all show excellent performance. All three techniques show good accuracy and speed. We will report on results and discuss future steps including models applicable to a wide class of tokamaks and applications of Generative AI and surrogate models for operators.