Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 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

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform 10Be Concentrations and Numerical Modeling

“…Successful implementation of this coupled model approach will allow for the constraint of the long-term geomorphic evolution of rocky coasts over a wide range of coastal cliff environments (and degree of shore platform preservation therein) in a manner that reduces equifinality that often arises from coastal evolution models due to (necessarily) simplified erosional processes (Shadrick et al, 2021). Best fit solutions are achieved via Metropolis Hastings Markov Chain Monte Carlo (MCMC, Adams et al, 2019;Estacio-Hiroms et al, 2016) simulations that iterate over a wide range of possible input values for the parameters most likely to influence shore platform evolution: material resistance, wave height decay rate, and intertidal weathering rate (Carr & Graff, 1982;Matsumoto et al, 2018), Through a simultaneous optimization of measured 10 Be concentrations and shore platform topography to modeled predictions of both, we estimate late Holocene (∼2 kyr) coastal cliff retreat rates for Del Mar, California and explore the influence of relative sea level rise, waves, and weathering on long-term coastal erosion at this site.…”

Freezing‐induced stiffness and strength anisotropy in freezing clayey soil: Theory, numerical modeling, and experimental validation

“…Readers are referred to Ref. [82] for more details of the optimization algorithm. The calibrated parameters are summarized in Table 2.…”

“…The benefits of the transversely isotropic formulations are further demonstrated by the displacement results of the samples, as shown in Figures 12 and 13, where simulation results with and without transverse isotropy are shown side by side. To introduce a control experiment, the material parameters of the simulations conducted via the isotropic model have been optimized with the Dakota package, 82 following the same procedure of the anisotropic scenario to minimize the least square errors for the same set of objective functions listed above.…”

The Sparse Grids Matlab kit -- a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification

“…Reference Webpage Dakota C++ [1] https://dakota.sandia.gov PyApprox Python https://pypi.org/project/pyapprox MUQ C++, Python [29] https://mituq.bitbucket.io UQLab Matlab [22] https://uqlab.com ChaosPy Python [12,11] https://chaospy.readthedocs.io SG++ Python, MATLAB, Java, C++ [30] https://sgpp.sparsegrids.org/ Spinterp Matlab [19,20] http://calgo.acm.org/847.zip 1 UQTk C++, Python [8,7] https://sandia.gov/uqtoolkit Tasmanian C++, Python, MATLAB, Fortran 90/95 [36,38,37] https://tasmanian.ornl.gov Table 1: Comparison of high-dimensional approximation / UQ-related software. how sparse grids are generated in the Sparse Grids Matlab Kit, how they are stored in memory (data structure) and what options are available for their generation.…”