Abstract
The proliferation of sensor networks in various areas of technology has enabled real-time behavioral monitoring of various physical systems in various length and time scales. The opportunity to use these data dynamically for improving speed, accuracy, and general performance of predictive behavior modeling simulation is of paramount importance. The present paper identifies enabling modeling methods and computational strategies that are critical for achieving real-time simulation response of very large and complex systems. It also discusses our choices of these technologies in the context of sample multidisciplinary computational mechanics applications.
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Keywords
- Proper Orthogonal Decomposition
- Computational Strategy
- Reduce Order Model
- Speed Index
- Computational Decision Support System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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Michopoulos, J., Farhat, C., Houstis, E. (2004). Dynamic-Data-Driven Real-Time Computational Mechanics Environment. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24688-6_90
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DOI: https://doi.org/10.1007/978-3-540-24688-6_90
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