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Dakota
Version 6.20
Explore and Predict with Confidence
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Dakota
Version 6.20
Explore and Predict with Confidence
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Main Page Table of Contents
The Dakota software (http://dakota.sandia.gov/) delivers advanced parametric analysis techniques enabling quantification of margins and uncertainty, risk analysis, model calibration, and design exploration with computational models. Dakota contains algorithms for optimization with gradient and nongradient-based methods, uncertainty quantification with sampling, reliability, stochastic expansion, and interval estimation methods, parameter estimation with nonlinear least squares methods, and sensitivity/variance analysis with design of experiments and parameter study capabilities. (Solution verification and Bayesian approaches are also in development.) These capabilities may be used on their own or as components within advanced algorithms such as surrogate-based optimization, mixed integer nonlinear programming, mixed aleatory-epistemic uncertainty quantification, 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 problem-solving environment for design and performance analysis of computational models on high performance computers.
The Developers Manual focuses on documentation of Dakota design principles and class structures; it derives principally from annotated source code. For information on Dakota input command syntax or features and capabilities, refer to the Users Manual at https://snl-github.io.
In Dakota, the environment manages execution modes and input/output streams and defines the top-level iterator. This top-level iterator may be either a standard iterator or a meta-iterator. In the former case, the iterator identifies a model and the environment executes the iterator on the model to perform a single study. In the latter case, iterator recursions are present and sub-iterators may identify their own models. In both cases, models may contain additional recursions in the case of nested iteration or surrogate modeling. In a simple example, a hybrid meta-iterator might manage a global optimizer operating on a low-fidelity model that feeds promising design points into a local optimizer operating on a high-fidelity model. And in a more advanced example, a surrogate-based optimization under uncertainty approach would employ an uncertainty quantification iterator nested within an optimization iterator and would employ truth models contained within surrogate models. Thus, iterators and models provide both stand-alone capabilities as well as building blocks for more sophisticated studies.
A model contains a set of variables, an interface, and a set of responses, and the iterator operates on the model to map the variables into responses using the interface. Each of these components is a flexible abstraction with a variety of specializations for supporting different types of iterative studies. In a Dakota input file, the user specifies these components through environment, method, model, variables, interface, and responses keyword specifications.
The use of class hierarchies provides a mechanism for extensibility in Dakota components. In each of the various class hierarchies, adding a new capability typically involves deriving a new class and providing a set of virtual function redefinitions. These redefinitions define the coding portions specific to the new derived class, with the common portions already defined at the base class. Thus, with a small amount of new code, the existing facilities can be extended, reused, and leveraged for new purposes. The following sections tour Dakota's class organization.
Class hierarchy: Environment.
Environments provide the top level abstraction for managing different execution modes and managing input and output streams. Specific environments include:
ExecutableEnvironment: the environment for execution of Dakota as a stand-alone application.
Class hierarchy: Iterator. Iterator implementations may choose to split operations up into run-time phases as described in Understanding Iterator Flow.
The iterator hierarchy contains a variety of iterative algorithms for optimization, uncertainty quantification, nonlinear least squares, design of experiments, and parameter studies. The hierarchy is divided into MetaIterator, Minimizer, and Analyzer branches.
The MetaIterator classes manage sequencing and collaboration among multiple methods with support for concurrent iterator parallelism. Methods include:
SeqHybridMetaIterator: hybrid minimization using a set of iterators employing a corresponding set of models of varying fidelity. The sequential hybrid passes the best solutions from one method in as the starting points of the next method in the sequence.
CollabHybridMetaIterator: hybrid minimization employing collaboration and sharing of response data among methods during the course if iteration. This class is currently a placeholder.
EmbedHybridMetaIterator: hybrid minimization involving periodic use of a local search method for refinement during the iteration of an outer global method. This class is currently a placeholder.
The Minimizer classes address optimization and deterministic calibration and are grouped into:
Optimization: Optimizer provides a base class for gradient-based (e.g., CONMINOptimizer and SNLLOptimizer) and derivative-free (e.g., NCSUOptimizer, JEGAOptimizer) optimization solvers. Most of these are wrappers for third-party libraries that implement the optimization algorithms. Classes APPSEvalMgr and COLINApplication provide the function evaluation interface for APPSOptimizer and COLINOptimizer, respectively.
Parameter estimation: LeastSq provides a base class for NL2SOLLeastSq, a least-squares solver based on NL2SOL, SNLLLeastSq, a Gauss-Newton least-squares solver, and NLSSOLLeastSq, an SQP-based least-squares solver.
The Analyzer classes are grouped into:
Uncertainty quantification: NonD provides a base class for non-deterministic methods in several categories:
Sampling: NonDSampling is further specialized with the NonDLHSSampling class for Latin hypercube and Monte Carlo sampling, and a number of other classes supporting incremental and adaptive sampling such as NonDAdaptImpSampling for multi-modal adaptive importance sampling.
Reliability Analysis: NonDReliability is further specialized with local and global methods (NonDLocalReliability and NonDGlobalReliability). NonDPOFDarts implements a computational geometry-based reliability method.
Stochastic Expansions: NonDExpansion includes specializations for generalized polynomial chaos (NonDPolynomialChaos) and stochastic collocation (NonDStochCollocation) and is supported by the NonDIntegration helper class, which supplies cubature, tensor-product quadrature and Smolyak sparse grid methods (NonDCubature, NonDQuadrature, and NonDSparseGrid).
Bayesian Calibration: NonDCalibration provides a base class for nondeterministic calibration methods with specialization to Bayesian calibration in NonDBayesCalibration, and specific implementations such as NonDQUESOBayesCalibration.
NonDInterval provides a base class for epistemic interval-based UQ methods. Three interval analysis approaches are provided: LHS (NonDLHSInterval), efficient global optimization (NonDGlobalInterval), and local optimization (NonDLocalInterval). Each of these three has specializations for single interval and Dempster-Shafer Theory of Evidence approaches.
Parameter studies and design of experiments: PStudyDACE provides a base class for ParamStudy, which provides capabilities for directed parameter space interrogation, PSUADEDesignCompExp, which provides access to the Morris One-At-a-Time (MOAT) method for parameter screening, and DDACEDesignCompExp and FSUDesignCompExp, which provide for parameter space exploration through design and analysis of computer experiments. NonDLHSSampling from the uncertainty quantification branch also supports design of experiments when in active all variables mode.
Solution verification studies: Verification provides a base class for RichExtrapVerification (verification via Richardson extrapolation) and other solution verification methods in development.
Class hierarchy: Model.
The model classes are responsible for mapping variables into responses when an iterator makes a function evaluation request. There are several types of models, some supporting sub-iterators and sub-models for enabling layered and nested relationships. When sub-models are used, they may be of arbitrary type so that a variety of recursions are supported.
SimulationModel: variables are mapped into responses using a simulation-based Interface object. No sub-iterators or sub-models are used.
SurrogateModel: variables are mapped into responses using an approximation. The approximation is built and/or corrected using data from a sub-model (the truth model) and the data may be obtained using a sub-iterator (a design of experiments iterator). SurrogateModel has two derived classes: DataFitSurrModel for data fit surrogates and HierarchSurrModel for hierarchical models of varying fidelity. The relationship of the sub-iterators and sub-models is considered to be "layered" since they are not used as part of every response evaluation on the top level model, but rather used periodically in surrogate update and verification steps.
NestedModel: variables are mapped into responses using a combination of an optional Interface and a sub-iterator/sub-model pair. The relationship of the sub-iterators and sub-models is considered to be "nested" since they are used to perform a complete iterative study as part of every response evaluation on the top level model.
Class hierarchy: Variables.
The Variables class hierarchy manages design, aleatory uncertain, epistemic uncertain, and state variable types for continuous, discrete integer, and discrete real domain types. This hierarchy is specialized according to how the domain types are managed:
MixedVariables: domain type distinctions are retained, such that separate continuous, discrete integer, and discrete real domain types are managed. This is the default Variable perspective, and draws its name from "mixed continuous-discrete" optimization.
RelaxedVariables: domain types are combined through relaxation of discrete constraints; i.e., continuous and discrete variables are merged into continuous arrays through relaxation of integrality (for discrete integer ranges) or set membership (for discrete integer or discrete real sets) requirements. The branch and bound minimizer is the only method using this approach at present.
Whereas domain types are defined based on the derived Variables class selection, the selection of active variable types is handled within each of these derived classes using variable views. These permit different algorithms to work on different subsets of variables. Data shared among Variables instances is stored in SharedVariablesData. For details on managing variables, see Working with Variable Containers and Views.
The Constraints hierarchy manages bound, linear, and nonlinear constraints and utilizes the same specializations for managing bounds on the variables (see MixedVarConstraints and RelaxedVarConstraints).
Class hierarchy: Interface.
Interfaces provide access to simulation codes or, conversely, approximations based on simulation code data. In the simulation case, an ApplicationInterface is used.
ApplicationInterface is specialized according to the simulation invocation mechanism, for which the following nonintrusive approaches are supported:
SysCallApplicInterface: the simulation is invoked using a system call (the C function system()). Asynchronous invocation utilizes a background system call. Utilizes the CommandShell utility.
ForkApplicInterface: the simulation is invoked using a fork (the fork/exec/wait family of functions). Asynchronous invocation utilizes a nonblocking fork.
SpawnApplicInterface: for Windows, fork is replaced by spawn. Asynchronous invocation utilizes a nonblocking spawn.
Fork and Spawn are inherited from ProcessHandleApplicInterface and System and ProcessHandle are inherited from ProcessApplicInterface. A semi-intrusive approach is also supported by:
Scheduling of jobs for asynchronous local, message passing, and hybrid parallelism approaches is performed in the ApplicationInterface class, with job initiation and job capture specifics implemented in the derived classes.
In the approximation case, global, multipoint, or local data fit approximations to simulation code response data can be built and used as surrogates for the actual, expensive simulation. The interface class providing this capability is
which is an essential component within the DataFitSurrModel capability described above in Models.
Class: Response.
The Response class provides an abstract data representation of response functions and their first and second derivatives (gradient vectors and Hessian matrices). These response functions can be interpreted as objective functions and constraints (optimization data set), residual functions and constraints (least squares data set), or generic response functions (uncertainty quantification data set). This class is not currently part of a class hierarchy, since the abstraction has been sufficiently general and has not required specialization.
A variety of services and utilities are used in Dakota for parallel computing, failure capturing, restart, graphics, etc. An overview of the classes and member functions involved in performing these services is included here.
Multilevel parallel computing: Dakota supports multiple levels of nested parallelism. A meta-iterator can manage concurrent iterators, each of which manages concurrent function evaluations, each of which manages concurrent analyses executing on multiple processors. Partitioning of these levels with MPI communicators is managed in ParallelLibrary and scheduling routines for the levels are part of IteratorScheduler, ApplicationInterface, and ForkApplicInterface.
Option management: Global options controlling behavior are managed in ProgramOptions, with the help of command-line option parsing in CommandLineHandler.
Parsing: Dakota employs NIDR (New Input Deck Reader) via Dakota::ProblemDescDB::parse_inputs to parse user input files. NIDR uses the keyword handlers in the NIDRProblemDescDB derived class to populate data within the ProblemDescDB base class, which maintains a DataEnvironment specification and lists of DataMethod, DataModel, DataVariables, DataInterface, and DataResponses specifications. Procedures for modifying the parsing subsystem are described in Instructions for Modifying Dakota's Input Specification.
Failure capturing: Simulation failures can be trapped and managed using exception handling in ApplicationInterface and its derived classes.
Restart: Dakota maintains a record of all function evaluations both in memory (for capturing any duplication) and on the file system (for restarting runs). Restart options are managed through ProgramOptions (with the help of CommandLineHandler); file management in OutputManager; and restart file insertions occur in ApplicationInterface. The dakota_restart_util executable, built from restart_util.cpp, provides a variety of services for interrogating, converting, repairing, concatenating, and post-processing restart files.
Memory management: Dakota employs the techniques of reference counting and representation sharing through the use of letter-envelope and handle-body idioms (Coplien, "Advanced C++"). The former idiom provides for memory efficiency and enhanced polymorphism in the following class hierarchies: Environment, Iterator, Model, Variables, Constraints, Interface, ProblemDescDB, and Approximation. The latter idiom provides for memory efficiency in data-intensive classes which do not involve a class hierarchy. The Response and parser data (DataEnvironment, DataMethod, DataModel, DataVariables, DataInterface, and DataResponses) classes use this idiom. When managing reference-counted data containers (e.g., Variables or Response objects), it is important to properly manage shallow and deep copies, to allow for both efficiency and data independence as needed in a particular context.
Graphics and Output: Dakota provides 2D iteration history graphics using Motif widgets. Graphics data can also be cataloged in a tabular data file for post-processing with 3rd party tools such as Matlab, Tecplot, etc. These capabilities are encapsulated within the Graphics class. An experimental results database is implemented in ResultsManager and ResultsDBAny. Options for controlling output and facilities for managing it are in OutputManager.
The following links provide guidance for core software components or specific development activities:
Additional development resources include:
1.8.20