Advanced regression analysis for estimation of fusion scaling laws

Scaling laws provide crucial information for the design specifications of next-step fusion devices such as ITER. They enable extrapolation of various plasma characteristics and performance measures from measurements assembled in international multi-machine databases. On the other hand, regression analysis is a ubiquitous instrument for exploration and characterization of relations between physical quantities. We have developed a new regression technique, called geodesic least squares (GLS), designed to handle significant uncertainty on the data and the regression model. It is based on minimization of the geodesic distance between the measured and predicted probability distribution of the response variable. With this technique we are contributing to the revision of historical multi-machine scaling laws for the energy confinement and the power threshold for the L-H transition. Other applications include modeling of the dependencies of intrinsic plasma rotation, ELM heat loads and width of the scrape-off layer.