Overview and status of the FES Scientific Machine Learning project, "Accelerating radio frequency modeling using machine learning"

Our machine learning (ML) project focuses 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 currect deposition profile. The later with its longer wavelengths uses physical options (full wave) also coupled with Fokker-Planck. The project aims to general surrogate models with machine learning to reduce simulation time to the order of milli-seconds (ms) needed for real time control during experimental discharges. The first part will develop a fast surrogate modelfor predicting RF heating and current drive with regression analysis using training data from raytracing calculations of LH waves in tokamaks. The second part will provide a ML generated solution to the inverse problem of relating line-integrated experimental bremsstrahlung measurements back to the associatedpower deposition and current drive profiles. This inverse problem does not have an analytic solution and the ML solution to it will provide real time information on the current profile. The third part involves accelerating full wave solvers by accelerating the inverse of the large matrices generated by that method. ML techniques will be used to find preconditioners to iteratively solver the matrices rather than use the more computationally intensive direct inversion. Once an efficient preconditioner is found, its is feasible to apply the first two methods to HHFW full wave models. Taken together, these three efforts result in ms times for surrogate model predictions suitable for real time control.