Bayesian approach for validation of runaway electron simulations

TL;DR

This paper introduces a Bayesian optimization framework utilizing surrogate models to efficiently validate runaway electron simulations in fusion plasma disruptions, reducing computational costs and improving uncertainty quantification.

Contribution

It presents a novel Bayesian approach combining Gaussian Process surrogate modeling with Bayesian optimization for validating runaway electron simulations.

Findings

Efficient validation of runaway electron models achieved.

Reduced number of simulations needed for parameter calibration.

Enhanced uncertainty quantification in plasma disruption modeling.

Abstract

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.