Automated Experimental Design of Safe Rampdowns via Probabilistic Machine Learning

Typically, the ramp-down phase of a shot consists of a decrease in current and injected power and optionally a change in shape, but there is considerable flexibility in the rate, sequencing, and duration of these changes. In this work, we give a procedure for automatically choosing experimental rampdown designs to rapidly converge to a good design of the rampdown phase using probabilistic machine learning methods and acquisition function techniques taken from Bayesian Optimization. In our campaign of experiments at DIII-D over the course of 2022, we found that there is a clear and statistically significant reduction in current and energy at the end of the rampdown in comparison to baseline operations when using rampdown designs generated by the model at the end of shots. We also find that the best action predicted by the model significantly improved as the model was able to explore over the course of the experimental campaign.