DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Reference Manual

By Michael S. Eldred, Anthony A. Giunta, B. V. B. Waanders, Steven F. Wojtkiewicz, William Hart, Mario Alleva. 2001

581 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8d14b7ba91bdff042de4b49b6fd70c5bd964891a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistical and Nonlinear Physics

1. Ronald D. Joslin
2. S. Collis
3. Avi Seifert
4. V. Theofilis

In Statistics and Probability

1. S. Reiter

Citations in the corpus

• A review on simulation-based optimization methods applied to building performance analysis (2014, background): “MatLab optimization toolboxes and Dakota [42] are not specifically designed for building simulation–optimization; thus these tools require more complex skills to use.”
• Issues in active flow control: theory, control, simulation, and experiment (2004, methodology): “Rigorous techniques for using model hierarchies are available in the context of engineering optimization [166], and as such, are also available for optimal design of active flow controllers and this is an important area for future research.”