Dakota
Dakota is an open-source software for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. It interfaces with simulation codes written in C, C++, Fortran, Python, and MATLAB.
optimization
simulation
uncertainty-quantification
sensitivity-analysis
Dakota: Open-Source Design Optimization Software
Design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis software - Dakota interfaces with simulation codes in C, C++, Fortran, Python, and MATLAB
What is Dakota?
Dakota (Design Analysis Kit for Optimization and Terascale Applications) is an extensible open-source software for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. It interfaces with simulation codes written in C, C++, Fortran, Python, and MATLAB.
Some key capabilities of Dakota include:
- Optimization with gradient and non-gradient-based methods, heuristic techniques, hybrid methods, and surrogate-based optimization
- Uncertainty quantification with sampling, reliability, stochastic expansion, and epistemic methods
- Parameter estimation using nonlinear least squares methods
- Sensitivity analysis with design of experiments and parameter study methods
Dakota is developed by Sandia National Laboratories and made available under the GNU Lesser General Public License. It leverages parallel computing across multiple processors and computer clusters to solve complex simulation-based analysis problems efficiently.
Some application areas where Dakota has been effectively used are engineering design, risk analysis, calibration of computer models, and quantification of margins and uncertainty in simulations. It continues to be enhanced with state-of-the-art algorithms and methods.
Dakota Features
Features
- Design optimization
- Uncertainty quantification
- Parameter estimation
- Sensitivity analysis
- Interfaces with multiple simulation software
Pricing
- Open Source
Pros
Open source
Wide range of analysis and optimization capabilities
Interfaces with many simulation codes
Active development community
Well documented
Cons
Steep learning curve
Requires coding/scripting for advanced features
Limited graphical user interface
Official Links
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