Practical Techniques for Machine Learning Control of Fusion Plasmas

Achieving controlled nuclear fusion requires complex and robust control systems, and significant interest has been directed towards machine learning and artificial intelligence as techniques for developing these controllers. We present three components that will be necessary for any successful machine learning based control system. The first is control oriented models that allow efficient and robust calculation of actuator signals in real time via Linear Recurrent Autoencoder Networks and quadratic programming. We also demonstrate methods for uncertainty quantification for machine learning models and methods to estimate the uncertainty and confidence intervals for predictive control models. Finally, we discuss the need for accurate estimates of the plasma state which can be obtained using machine learning models for approximate Bayesian inference combining both irregularly sampled measurements and dynamical models of plasma behavior.