Least squares shadowing sensitivity analysis of a modified Kuramoto-Sivashinsky equation
Computational methods for sensitivity analysis are invaluable tools for scientists and engineers investigating a wide range of physical phenomena. However, many of these methods fail when applied to chaotic systems, such as the Kuramoto-Sivashinsky (K-S) equation, which models a number of different...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier,
2016-08-08T19:56:01Z.
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| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | Computational methods for sensitivity analysis are invaluable tools for scientists and engineers investigating a wide range of physical phenomena. However, many of these methods fail when applied to chaotic systems, such as the Kuramoto-Sivashinsky (K-S) equation, which models a number of different chaotic systems found in nature. The following paper discusses the application of a new sensitivity analysis method developed by the authors to a modified K-S equation. We find that least squares shadowing sensitivity analysis computes accurate gradients for solutions corresponding to a wide range of system parameters. United States. Air Force Office of Scientific Research (AFSOR Award F11B-T06-0007) United States. National Aeronautics and Space Administration (NASA Award NNH11ZEA001N) United States. Department of Defense (NDSEG fellowship) |
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