Presenters-0143: Difference between revisions
From CSDMS
m Add youtube views template if missing |
m Text replacement - "\|CSDMS meeting youtube views=\{\{(Youtube_[^}]+)\}\}" to "|CSDMS meeting youtube views={{#explode:{{$1}}| |0}} |CSDMS meeting youtube AverageViews={{#explode:{{$1}}| |1}}" |
||
| Line 14: | Line 14: | ||
|CSDMS meeting abstract presentation=Dakota is an open-source toolkit with several types of algorithms, including sensitivity analysis (SA), uncertainty quantification (UQ), optimization, and parameter calibration. Dakota provides a flexible, extensible interface between computational simulation codes and iterative analysis methods such as UQ and SA methods. Dakota has been designed to run on high-performance computing platforms and handles a variety of parallelism. In this clinic, we will provide an overview of Dakota algorithms, specifically focusing on uncertainty quantification (including various types of sampling, reliability analysis, stochastic expansion, and epistemic methods), sensitivity analysis (including variance-based decomposition methods and design of experiments), and parameter calibration (including nonlinear least squares and Bayesian methods). The tutorial will provide an overview of the methods and discuss how to use them. In addition, we will briefly cover how to interface your simulation code to Dakota. | |CSDMS meeting abstract presentation=Dakota is an open-source toolkit with several types of algorithms, including sensitivity analysis (SA), uncertainty quantification (UQ), optimization, and parameter calibration. Dakota provides a flexible, extensible interface between computational simulation codes and iterative analysis methods such as UQ and SA methods. Dakota has been designed to run on high-performance computing platforms and handles a variety of parallelism. In this clinic, we will provide an overview of Dakota algorithms, specifically focusing on uncertainty quantification (including various types of sampling, reliability analysis, stochastic expansion, and epistemic methods), sensitivity analysis (including variance-based decomposition methods and design of experiments), and parameter calibration (including nonlinear least squares and Bayesian methods). The tutorial will provide an overview of the methods and discuss how to use them. In addition, we will briefly cover how to interface your simulation code to Dakota. | ||
|CSDMS meeting youtube code=0 | |CSDMS meeting youtube code=0 | ||
|CSDMS meeting youtube views={{Youtube_0}} | |CSDMS meeting youtube views={{#explode:{{Youtube_0}}| |0}} | ||
|CSDMS meeting youtube AverageViews={{#explode:{{Youtube_0}}| |1}} | |||
|CSDMS meeting participants=0 | |CSDMS meeting participants=0 | ||
}} | }} | ||
Latest revision as of 16:34, 11 June 2025
CSDMS 2014 annual meeting - Uncertainty and Sensitivity in Surface Dynamics Modeling
Dakota: A Toolkit for Sensitivity Analysis, Uncertainty Quantification, and Calibration
Abstract
Dakota is an open-source toolkit with several types of algorithms, including sensitivity analysis (SA), uncertainty quantification (UQ), optimization, and parameter calibration. Dakota provides a flexible, extensible interface between computational simulation codes and iterative analysis methods such as UQ and SA methods. Dakota has been designed to run on high-performance computing platforms and handles a variety of parallelism. In this clinic, we will provide an overview of Dakota algorithms, specifically focusing on uncertainty quantification (including various types of sampling, reliability analysis, stochastic expansion, and epistemic methods), sensitivity analysis (including variance-based decomposition methods and design of experiments), and parameter calibration (including nonlinear least squares and Bayesian methods). The tutorial will provide an overview of the methods and discuss how to use them. In addition, we will briefly cover how to interface your simulation code to Dakota.
Please acknowledge the original contributors when you are using this material. If there are any copyright issues, please let us know (CSDMSweb@colorado.edu) and we will respond as soon as possible.
Of interest for:
