A general framework for quantifying uncertainty at scale

Abstract

In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful supercomputers, and therefore only a limited number of cases can be investigated explicitly. This prevents straightforward approaches to important tasks like uncertainty quantification and sensitivity analysis. This challenge can be overcome via our recently developed sensitivity-driven dimension-adaptive sparse grid interpolation strategy. The method exploits, via adaptivity, the structure of the underlying model (such as lower intrinsic dimensionality and anisotropic coupling of the uncertain inputs) to enable efficient and accurate uncertainty quantification and sensitivity analysis at scale. Here, we demonstrate the efficiency of this adaptive approach in the context of fusion research, in a realistic, computationally expensive scenario of turbulent transport in a magnetic confinement tokamak device with eight uncertain parameters, reducing the effort by at least two orders of magnitude. In addition, we show that this refinement method intrinsically provides an accurate surrogate model that is nine orders of magnitude cheaper than the high-fidelity model.

Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles

“…Due to the cost associated to retrieving large simulation databases to be adopted as training sets for these NNs, previous works have focused on spanning a small volume in the input space. This restricts some of the current applications to small dimensionality and narrow range in parameter space [17,18], or medium dimensionality [10], also sometimes based on experiments [13,19]. At the same time, in [20], where linear GKW [21] simulations were used to derive semi-empirical saturation rules based on JT60 discharges, an increase in data availability was indicated as a major contributor to the success of the derived reduced-order model in integrated models.…”

Modeling electron temperature profiles in the pedestal with simple formulas for ETG transport

“…Recently, ETG studies have focused on the pedestal [1,[5][6][7][8][9][10][11][12][13][14][15][16][17], where ETG appears to produce experimentally relevant transport levels in many scenarios. Several recent papers have formulated reduced models and/or simple algebraic expressions for ETG transport in the pedestal [12,14,18,19]. This paper presents simple, yet accurate, algebraic expressions for ETG transport in the pedestal improving on those described in [18].…”