Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

Abstract

The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with respect to various physical parameters characterizing the background plasma and the magnetic equilibrium. Therefore, uncertainty quantification is essential for achieving predictive numerical simulations of plasma turbulence. However, the high computational costs of the required gyrokinetic simulations and the large number of parameters render standard Monte Carlo techniques intractable. To address this problem, we propose a multi-fidelity Monte Carlo approach in which we employ data-driven low-fidelity models that exploit the structure of the underlying problem such as low intrinsic dimension and anisotropic coupling of the stochastic inputs. The low-fidelity models are efficiently constructed via sensitivity-driven dimension-adaptive sparse grid interpolation using both the full set of uncertain inputs and subsets comprising only selected, important parameters. We illustrate the power of this method by applying it to two plasma turbulence problems with up to $14$ stochastic parameters, demonstrating that it is up to four orders of magnitude more efficient than standard Monte Carlo methods measured in single-core performance, which translates into a runtime reduction from around eight days to one hour on 240 cores on parallel machines.

28 pages, 9 figures, 11 tables

References in corpus (4)

• New high-confinement regime with fast ions in the core of fusion plasmas
• Turbulence suppression by energetic particles: A sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization
• Context-aware surrogate modeling for balancing approximation and sampling costs in multi-fidelity importance sampling and Bayesian inverse problems
• A sparse-grid probabilistic scheme for approximation of the runaway probability of electrons in fusion tokamak simulation

Cited by in corpus (3)

• Surrogate modelling and uncertainty quantification based on multi-fidelity deep neural network
• Accelerating the estimation of energetic particle confinement statistics in stellarators using multifidelity Monte Carlo
• Fast prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decomposition