Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

Abstract

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers.This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies. 4 18 CONTENTS

Material-Specific Optimization of Gaussian Basis Sets against Plane Wave Data

“…The reference calculations were performed in a plane wave basis in GPAW 54 with an energy cutoff of 2000 eV and a Monkhorst−Pack grid of 8 × 8 × 8 k-points. We use the DAKOTA 41 conmin-frcg DAKOTA coliny-pattern-search package as optimizers. For all cases analyzed, the optimizer converges within 100 optimization steps.…”

“…This uncontracted basis is employed as a starting guess for the energy minimization in an atomic Hartree–Fock calculation. We use a conjugate gradient minimization , scheme to find the variational parameters { A k } that minimize the electronic energy of the atom. Subsequently, the atom parameters are reoptimized in the configuration interaction single and double (CISD) method.…”

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

“…First, an appropriate initial condition is obtained by spinning up the E3SM to equilibrium, as discussed in Section 2.2. Next, after selecting T spin‐up and T final (ensuring that these values are large enough to avoid initial transients in the ensemble runs), we employ the DAKOTA library (Adams et al., 2013) to generate N random samples of the parameters { z i } from the selected parameter ranges or probability distributions (Table 2). We then create namelist files for each of our E3SM runs, corresponding to each of the N randomly selected parameter sets (for our study, the relevant namelist files are user_nl_cam, user_nl_mpaso, and user_nl_mpascice), and set off N runs of the E3SM, branching off the spun‐up initial condition.…”

“…Our GSA is based upon random realizations of the nine parameters, randomly selected from a uniform distribution over the ranges defined by the “Min” and “Max” values given in Table 2. The sampling and associated model evaluations were managed using the DAKOTA library (Adams et al., 2013), an open‐source software package for optimization, UQ, and advanced parametric analysis. Much like the parameters themselves, the selection of the parameter ranges was guided by past analyses (Asay‐Davis et al., 2018; Qian et al., 2018; Rasch et al., 2019; Reckinger et al., 2015; Urrego‐Blanco et al., 2016, 2019).…”

Determining the Effect of Varying Magmatic Volatile Content on Lunar Magma Ascent Dynamics

“…We conducted a sensitivity analysis to determine the relationship between various input parameters (inlet temperature and pressure, H 2 content, H 2 O content, CO content, and conduit radius) and model outputs for quantities within the conduit (gas volume fraction, exit pressure, exit velocity, and mass flow rate). The sensitivity analysis was performed using the Dakota toolkit (Adams et al., 2014), which is an open‐source software produced by Sandia National Laboratories. The software has a broad range of uses including model calibration, risk analyses, and uncertainty quantification.…”