  Plasma-terminating disruptions in future fusion reactors may result in
conversion of the initial current to a relativistic runaway electron beam.
Validated predictive tools are required to optimize the scenarios and
mitigation actuators to avoid the excessive damage that can be caused by such
events. Many of the simulation tools applied in fusion energy research require
the user to specify several input parameters that are not constrained by the
available experimental information. Hence, a typical validation exercise
requires multiparameter optimization to calibrate the uncertain input
parameters for the best possible representation of the investigated physical
system. The conventional approach, where an expert modeler conducts the
parameter calibration based on domain knowledge, is prone to lead to an
intractable validation challenge. For a typical simulation, conducting
exhaustive multiparameter investigations manually to ensure a globally optimal
solution and to rigorously quantify the uncertainties is an unattainable task,
typically covered only partially and unsystematically. Bayesian inference
algorithms offer a promising alternative approach that naturally includes
uncertainty quantification and is less subjective to user bias in choosing the
input parameters. The main challenge in using these methods is the
computational cost of simulating enough samples to construct the posterior
distributions for the uncertain input parameters. This challenge can be
overcome by combining probabilistic surrogate modelling, such as Gaussian
Process regression, with Bayesian optimization, which can reduce the number of
required simulations by several orders of magnitude. Here, we implement this
type of Bayesian optimization framework for a model for analysis of disruption
runaway electrons, and explore for simulations of current quench in a JET
plasma discharge with an argon induced disruption.
