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UNCERTAINTY QUANTIFICATIONQuantifying Parametric Uncertainty in the Rothermel Model, with E. Jimenez and S. Goodrick, International Journal of Wildland Fire, 2008, 17(3): **-** . Multiobjective Stochastic Control in Fluid Dynamics via Game Theory Approach. Application to Periodic Burgers Equation, with A.-M. Croicu, J. Optimization Theory and Applications2008, 138(2): **. Uncertainty Quantification of Rothermel Model Using a Sensitivity Derivative Enhanced Sampling Method, with E. Jimenez and S. Goodrick, Proceedings of the 2nd Fire Behavior and Fuels Conference: The Fire Environment -- Innovations, Management, and Policy, 2007, Destin, Florida, March 26-30, 2007. Application of evidence theory to quantify uncertainty in hurricane/typhoon track forecasts, with S.V. Poroseva, and J. Letschert, Meteorology and Atmospheric Physics, 2007, 97: 149-169. An efficient sampling method for stochastic inverse problems, with P. Ngnepieba, Comput. Optim. Appl., 2007, 37 :121-138. Application of Evidence Theory to Quantify Uncertainty in Forecast of Hurricane Path, with S.V. Poroseva, and J. Letschert, Proceed. of the 18th Conference on Probability and Statistics, the American Meteorological Society 86th Annual Meeting, 29 January-2 February 2006 (Atlanta, GA). Optimal Control and Stochastic Parameter Estimation, with P. Ngnepieba and L. Debreu, Monte Carlo Methods and Applications, 2006, 12 (5): 461-476. On improving the predictive capability of turbulence models using evidence theory, with S.V. Poroseva, and S.L. Woodruff, AIAA J. 2006, 44: 1220-1228 . A variance reduction method based on sensitivity derivatives, with Y. Cao, T. A. Zang and A. Zatezalo, Applied Numerical Mathematics 2006, 56: 800-813. A Systematic Approach for Quantifying and Improving CFD Computations of Complex Flows, with S.V. Poroseva and S.L. Woodruff, Proceed. of the 4th Int. Symp. on Turbulence and Shear Flow Phenomena, June 2005 (Williamsburg, Virginia), 2005, 2: 543-548. Stochastic approaches to uncertainty quantification in CFD simulations, with L. Mathelin and T.A. Zang, Numerical Algorithms, 2005, 38: 209-236. On improving the predictive capability of turbulence models using evidence theory, with S.V. Poroseva, and S.L. Woodruff, in Proceeding of the 43rd AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2005-1096, January, 2005, Reno, NV . Uncertainty propagation for a turbulent compressible nozzle flow using stochastic methods, with L. Mathelin, T. A. Zang and F. Bataille, AIAA J.,2004, 42(8): 1669-1676. An efficient sampling method for stochastic optimal control problem, In Proceeding of 10th International Conference on Information Analysis and Synthesis ISAS 2004 and the International Conference on Cybernetics and Information Technologies, Systems and Applications: CITSA 2004. CITSA, Orlando, Florida, pp. 375-380, July 2004. On the exploitation of sensitivity derivatives for improving sampling methods, with Y. Cao and T. A. Zang, AIAA J., 2004, 42(4): 815-822. A Stochastic Collocation algorithm for uncertainty analysis, with L. Mathelin, NASA/CR-2003-212153, 2003. An efficient Monte Carlo method for optimal control problems with uncertainty, with Y. Cao and T. A. Zang, Computational Optimization and Application, 2003, 26(3): 219-230. A numerical simulation of the appearance of chaos in finite-length Taylor-Couette flow, with C. Streett, Appl. Numer. Math., 1991, 7(1): 41-72. |