Publications
Publication | Type | Year |
---|---|---|
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 Theory Manual |
SAND Report – 2020 SAND Report | 2020 |
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User?s Manual |
SAND Report – 2020 SAND Report | 2020 |
Dakota Sensitivity Analysis and Uncertainty Quantification, with ExamplesCDSE Days |
Conference Paper – 2015 Conference Paper | 2015 |
Dakota: A Toolkit for Sensitivity Analysis, Uncertainty Quantification, and CalibrationCDSE Days
|
Abstract – 2015 Abstract | 2015 |
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 6.0 Users ManualVersion 6.0 Users Manual |
SAND Report – 2014 SAND Report | 2014 |
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 6.0 Theory ManualVersion 6.0 Theory Manual |
SAND Report – 2014 SAND Report | 2014 |
Gaussian Process Adaptive Importance Sampling (GPAIS)SIAM Conference on Uncertainty Quantification |
Conference Paper – 2014 Conference Paper | 2014 |
Efficient and Robust Gradient Enhanced Kriging Emulators |
SAND Report – 2013 SAND Report | 2013 |
Gaussian Process Adaptive Importance SamplingInternational Journal for Uncertainty Quantification |
Journal Article – 2013 Journal Article | 2013 |
Fast Generation of Space-filling Latin Hypercube Sample Designs13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference |
Conference Paper – 2013 Conference Paper | 2013 |
Gaussian Process Adaptive Importance Sampling (GPAIS)International Journal for Uncertainty Quantification |
Journal Article – 2012 Journal Article | 2012 |
Gaussian Process Adaptive Importance Sampling (GPAIS)Physical Review Letters |
Journal Article – 2012 Journal Article | 2012 |
Gaussian Process Adaptive Importance Sampling (GPAIS)Quality and Productivity Research Conference 2012 |
Conference Paper – 2012 Conference Paper | 2012 |
Effective and Efficient Handling of Ill - Conditioned Correlation Matrices in Kriging and Gradient Enhanced Kriging Emulators Through Pivoted Cholesky FactorizationSIAM Conference on Uncertainty Quantification |
Conference Paper – 2012 Conference Paper | 2012 |
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis; Version 5.2 Theory Manual |
SAND Report – 2011 SAND Report | 2011 |
Towards Breaking the Curse of Dimensionality Through Efficient and Robust Gradient Enhanced Kriging EmulatorsComputer Methods in Applied Mechanics and Engineering |
Journal Article – 2011 Journal Article | 2011 |
Progress Towards Nested Space and Sub-Space Filling Latin Hypercube Sample Designs11th US National Congress on Computational Mechanics |
Conference Paper – 2011 Conference Paper | 2011 |
The Effect of Sample Design Quality on Kriging Emulator PredictionsStatistics and Computing |
Journal Article – 2011 Journal Article | 2011 |
Surrogate Modeling with SurfpackINFORMS Annual Meeting 2010 |
Conference Paper – 2011 Conference Paper | 2011 |
Fast Generation of Nested Space-filling Latin Hypercube Sampling Designs2011 SIAM Conference on Computational Science and Engineering |
Conference Paper – 2011 Conference Paper | 2011 |
Fast Generation of Nested Space-filling Latin Hypercube Sample DesignsIMAC XXIX A Conference and Exposition on Structural Dynamics |
Conference Paper – 2011 Conference Paper | 2011 |
Generation of Pareto Optimal Ensembles of Calibrated Parameter Sets for Climate ModelsAmerican Geophysical Union's Fall Meeting 2010 |
Conference Paper – 2010 Conference Paper | 2010 |
Fast Generation of Space-filling Latin Hypercube Sample Designs13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference |
Conference Paper – 2010 Conference Paper | 2010 |
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 5.0 Users ManualQuantification, and Sensitivity Analysis: Version 5.0 Users Manual |
SAND Report – 2010 SAND Report | 2010 |
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 5.0 Reference ManualQuantification, and Sensitivity Analysis: Version 5.0 Reference Manual |
SAND Report – 2010 SAND Report | 2010 |
Document Title | Type | Year |