Version 5.0 (2009/12/21)
DAKOTA changes license to the GNU Lesser General Public License to enable library mode users, incorporating license changes for several TPLs
All new JAGUAR 2.0 graphical user interface for creating, editing, and running DAKOTA input files (BSD-like license)
Additional discrete range and discrete set types within design, uncertain, and state variables. New types supported by parameter studies, nondeterministic sampling, JEGA, and COLINY.
Anisotropic sparse grids, numerically-generated orthogonal polynomials, and improved expansion tailoring for stochastic expansion UQ methods
New methods for epistemic and mixed aleatory-epistemic uncertainty quantification: local/global interval estimation and local/global evidence.
Creation and management of evaluation working directories
SNL: DAKOTA modules on compute clusters (module avail dakota)
JAGUAR 2.0 graphical user interface, based on Eclipse Workbench, with text- and graphical-based editing, templates, sensitivity analysis wizard, and error checking. Available via separate download after registering for DAKOTA download.
Capability to specify working directories, template directories, and lightweight linking for system and fork interfaces.
DAKOTA input files can use Matlab-style sequence notation (L:S:U and beyond) to specify variable ranges; improved tolerance for whitespace in input files
Improved DAKOTA + application parallelism examples; new asynchronous local evaluation static scheduling for improved parallel tiling on clusters.
Pre-run (with optional variables output) and post-run (with variables/responses input) modes supported by sampling, paramter study, and DACE methods
Examples of Matlab and Python interfaces
Automated cleanup of DAKOTA temporary files on unexpected exit
Refactor of Variables and Constraints hierarchies to manage continuous, discrete integer, and discrete real domains among design, aleatory uncertain, epistemic uncertain, and state types.
Additional discrete design variable types: discrete design integer range, discrete design integer set, discrete design real set
New discrete uncertain distibutions: poisson, binomial, negative binomial, geometric, hypergeometric
Additional discrete state variable types: discrete state integer range, discrete state integer set, discrete state real set
Updates to specification of continuous and discrete histograms
Alternate specification for lognormal using lambdas and zetas
New capability to perform parameter studies and sampling over discrete variables. New discrete variable types also supported in JEGA and COLINY.
DACE and parameter study classes (DDACE, FSUDACE, and multi-dimensional parameter study) can have correlations calculated and printed in addition to sampling methods
Improved accuracy and robustness in correlation computations
PSUADE: use updated (Campolongo 2007) sample generation scheme to improve space-filling properties
Improved EGO convergence controls (based on nearest neighbor)
wrapper class for LANL’s GPMSA code (Bayesian calibration)
importance sampling capability
nonlinear conjugate gradient optimization solver using Trilinos vector-matrix utilities
hang in DDACE orthogonal array LHS
OPT++ NIP methods not respecting bound constraints
New example problems for multifidelity OUU (MVFOSM as low fidelity UQ and stochastic expansion as high fidelity UQ), epistemic UQ, mixed aleatory-epistemic UQ, discrete sampling and parameter studies, etc.
Uncertainty Quantification (UQ) Capabilities:
Stochastic expansions (polynomial chaos expansion (PCE) and stochastic collocation (SC)):
Simplified controls for PCE: expansion formulation now inferred or estimated from quadrature_order or sparse_grid_level specifications. Automatic expansion tailoring minimizes performance loss due to poor expansion/integration synchronization.
Addition of numerically-generated orthogonal polynomials for generating optimal basis for non-Askey distribution types (uses Gauss-Wigert or discretized Stieltjes procedures for polynomial recursion coefficients in combination with Golub-Welsch for Gauss point/weight computation)
Addition of analytic variance-based decomposition for global sensitivity analysis using method of Sobol indices
Addition of analytic covariance among multiple response functions.
Addition of anisotropic Smolyak sparse grids, with user-supplied dimension preference.
Revision of Gaussian quadrature rules within sparse grids to use linear growth, providing finer grain control and more uniform integrand coverage.
PCE customizations for sparse grids: exclusive usage of linear growth rules (no Clenshaw-Curtis or Gauss-Patterson), total-order expansions for isotropic, custom expansion for anisotropic.
Addition of Askey and Wiener basis polynomial over-rides.
Modified variable correlation logic to revert to Wiener expansions for de-correlation as needed on a per-variable basis.
all_variables mode now includes epistemic uncertain variables (previously design and state only) using a Legendre basis.
Epistemic UQ methods:
Refactor of NonDInterval hierarchy to generalize and incorporate new methods.
Evidence: new global (LHS or EGO) and local (SQP or NIP) methods to calculate belief and plausibility in evidence theory calculations. Removed dependence on legacy Fortran package.
Interval estimation: new global (LHS or EGO) and local (SQP or NIP) methods to calculate response output intervals.
Extension of active Variables/Constraints views to support aleatory, epistemic, or aggregated aleatory-epistemic uncertain views.
Mixed aleatory-epistemic UQ methods:
New nested approaches (e.g. second-order probability, mixed evidence) where the outer loop (e.g. interval estimation or evidence) can leverage analytic moments and their sensitivities with respect to epistemic parameters.
Eliminated dependence on GSL in favor of Boost (to eliminate GNU GPL dependency)
Fully deployed Teuchos for all numerical data types. Boost and STL are used for all bookkeeping types.
Better out-of-source (VPATH) builds and documentation, including management of Boost and Teuchos
Switched from RNUM2 to Boost MT19937 random number generator for longer period, with run-time selection option
Deployed Boost multi-index containers to evaluation queue management, supporting multiple indexing options for optimized lookups.
Lightweight active/inactive data views implemented with Teuchos and Boost multi-array to eliminate deep data copies.
Updates to third-party libraries: JEGA, PSUADE, AMPL, Boost, Pecos, Teuchos
Minimal data mode for variables and responses omits labels, types, ids in some contexts for better scalability
Unified bounds checking for debugging and reduced overhead in optimized builds
Binary distributions for Mac OS X and Cygwin, including library dependencies
Better detection of MPICH2 and shmem communicators
Automatic syncronization of DAKOTA input specification help docs to JAGUAR
Surfpack: improved random seed management for repeatability.
Better handling of numerical comparisons on 32-bit
Finite differencing honors variable bounds; more efficient finite difference Hessians
PSUADE generally available under LGPL
Surfpack and LHS relicensed under LGPL
DLL API improvements for re-entry, error handling, get/set options
ModelCenter, MATLAB, and Python interface updates to latest API
Updates to Matlab memory management
HTTP-based usage tracking (disabled by default; currently used Sandia internally only)
Windows: use spawn as alternative to fork