Compressed Sensing and Time-Parallel Reduced-Order Modeling for Structural Health Monitoring Using a DDDAS

Authors:
J. Cortial;C. Farhat;L. J. Guibas;M. Rajashekhar
Affiliations:
Institute for Computational and Mathematical Engineering,;Institute for Computational and Mathematical Engineering, and Department of Mechanical Engineering,;Department of Computer Science, Stanford University, Stanford, CA 94305, U.S.A;Department of Computer Science, Stanford University, Stanford, CA 94305, U.S.A
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Year:
2007

Citing 4
Cited 2

On a data-driven environment for multiphysics applications

Future Generation Computer Systems - Special section: Complex problem-solving environments for grid computing
DDEMA: a data driven environment for multiphysics applications

ICCS'03 Proceedings of the 2003 international conference on Computational science
Towards a dynamic data driven system for structural and material health monitoring

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Compressed sensing

IEEE Transactions on Information Theory

Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology

Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Dynamic Data Driven Application System for Plume Estimation Using UAVs

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses recent progress achieved in two areas related to the development of a Dynamic Data Driven Applications System (DDDAS) for structural and material health monitoring and critical event prediction. The first area concerns the development and demonstration of a sensor data compression algorithm and its application to the detection of structural damage. The second area concerns the prediction in near real-time of the transient dynamics of a structural system using a nonlinear reduced-order model and a time-parallel ODE (Ordinary Differential Equation) solver.