UNCERTAINTY AND SENSITIVITY ANALYSES
A framework for model analysis across multiple experiment regimes: Investigating effects of Zinc on Xylella fastidiosa as a case study , with M. Aggarwal, L. De La Fuente, F. Navarrete, and N. G. Cogan, Journal of Theoretical Biology, submited 2018, **: ***-***.
Fractional Viscoelasticity in Fractal Media: Theory, Experimental Validation, and Uncertainty Analysis, with S. Mashayekhi, P. Miles, and W. Oates, Journal of the Mechanics and Physics of Solids, 2018, 111: 134-156.
Uncertainty Propagation in a Model of Dead-end Bacterial Microfiltration Using Fuzzy Interval Analysis , with N. Cogan and S. Chellam, J. Membrane Sci., 2018, 546: 215-224.
Two Mthods of Global Sensitivity Analysis Used to Investigate Parameters for MRSA Nasal Carriage Model, with A. Jarrett and N. Cogan, Bull. Math. Biol., 2017, 79: 2258-2272.
Short-term Antiretroviral Treatment Recommendations Based on Sensitivity Analysis of a Mathematical Model for HIV Infection of CD4+T Cells , with A.-M. Croicu, A. Jarrett, and N. Cogan, Bull. Math. Biol., 2017, 79: 2649-2671.
Rate dependent constitutive behavior of dielectric elastomers and applications in legged robotics, with W. Oates, P. Miles, W. Gao, J. Clark, and S. Mashayekhi, Proc. SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, Portland, Oregan, March 26-29, 2017.
An Efficient Method for Mixed types of Uncertainty Propagation Using Dempster-Shafer Theory, with Y. He, submitted, 2016.
Uncertainty Analysis of Dielectric Elastomer Membranes Under Electromechanical Loading, with P. Miles, A. Moura, W. Gao and W. Oates, Smart Materials and Structures, submitted, 2016.
Accurate Construction of High Dimensional Model Representation with Application to Uncertainty Quantification, with Y. Liu, and G. Okten, Reliability Engineering and System Safety, 2016, 152: 281-295.
Sensitivity Analysis of a Pharmacokinetic Model of Vaginal Anti-HIV Microbicide Drug Delivery, with A. Jarrett, Y. Gao, N. Cogan and D. Katz, Journal of Pharmaceutical Sciences, 2016, 105: 1772-1778.
Optimal Unified Combination Rule in Application of Dempster-Shafer theory to Lung Cancer Radiotherapy Dose Response Outcome Analysis , with Y. He, Y. Gong, and Y. Xiao, Journal of Applied Clinical Medical Physics, 2016, 17(1): 4-11.
Accurate Construction of High Dimensional Model Representation with Application to Uncertainty Quantification, with Y. Liu, and G. Okten, Reliability Engineering and System Safety, submitted, 2015.
Two Mthods of Global Sensitivity Analysis Used to Investigate Parameters for MRSA Nasal Carriage Model, with A. Jarrett and N. Cogan, submitted 2015.
Sensitivity Analysis of a Pharmacokinetic Model of Vaginal Anti-HIV Microbicide Drug Delivery, with A. Jarrett, Y. Gao, N. Cogan and D. Katz, Journal of Pharmaceutical Sciences, submitted, 2015.
Mathematical Model for MRSA Nasal Carriage, with A. Jarrett and N. Cogan, Bulletin of Mathematical Biology, 2015, 77(9): 1787-1812.
Global Sensitivity Analysis Used to Interpret Biological Experimental Results, with A. Jarrett, Y. Liu, and N. Cogan, Journal of Mathematical Biology, 2015, 71: 151-170.
Uncertainty and Robustness in Weather Derivative Models, with A. Goncu, Y. Liu, and G. Okten, in Monte Carlo and Quasi-Monte Carlo Methods 2014 , Ronald Cools and Dirk Nuyens (editors), Springer-Verlag, 2015.
Global sensitivity analysis for the Rothermel model based on high-dimensional model representation, with Y. Liu and G. Okten, Canadian Journal of Forest Research, 2015, 45: 1-6.
Parametric Uncertainty Quantification in the Rothermel Model with Randomized Quasi-Monte Carlo Methods, with Y. Liu, E. Jimenez, G. Okten, and S. Goodrick, International Journal of Wildland Fire, 2015, 24: 307-316.
Application of Dempster-Shafer theory in dose response outcome analysis Part II, with Y. He, Y. Gong, and Y. Xiao, Phys. Med. Biol., submitted 2014.
An Optimal Unified Combination Rule, with Y. He, BELIEF2014: 3rd International Conference on Belief Functions, Oxford, UK, September 26-28, 2014, Proceedings, F. Cuzzolin (editor), Lecture Notes in Artificial Intelligence, 2014, 8764: 39-48.
Uncertainty and Robustness in Weather Derivative Models, with A. Goncu, Y. Liu, and G. Okten, Proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, KU Leuven, Belgium, April 8 - 11, 2014.
Global Sensitivity Analysis for the Rothermel Model based on High Dimensional Model Representation, with Y. Liu and G. Okten, Proceedings of the 4th Fire Behavior and Fuels Conference, 18 - 21 February 2013, Raleigh, North Carolina, Dale Wade and Rebekah Fox (editors) (International Association of Wildland Fire: Missoula, MT) 2013: 51-60.
A Variance Reduction Method Based on Sensitivity Derivatives, Part 2, with E. Jimenez and Y. Liu, Applied Numerical Mathematics, 2013, 74: 151-159.
Optimization of a Monte Carlo Variance Reduction Method Based on Sensitivity Derivatives, with Y. Liu, and G. Okten, Applied Numerical Mathematics, 2013, 72(1): 160-171.
Shape Optimization Under Uncertainty, with A.-M. Croicu, G. Klopfer and A. Jameson, AIAA Journal, 2012, 50(9): 1905-1919.
Application of Dempster-Shafer theory in dose response outcome analysis, with W. Chen, Y. Cui, Y. He, Y. Yu, J. Galvin, and Y. Xiao, Phys. Med. Biol., 2012, 57(2): 5575-5585.
Uncertainty Quantification in the Horizontal Projection of Flight Plan Trajectories Using Evidence Theory, with S. V. Poroseva, Y. He, and R. Mankbadi, Paper AIAA 2011-1759, 13th AIAA Non-Deterministic Approaches Conference, Denver, Colorado, 4-7 April 2011.
Stochastic data assimilation with a polynomial chaos parametric estimation, with L. Mathelin and C. Desceliers, Computational Mechanics, 2011, 47: 603-616.
Application of Evidence Theory in Radiation Oncology Outcome Analysis, with Y. Xiao, Y. Cui, W. Chen, Y. Yu and J. Galvin, Proc. 3rd Intern. Conf. Biomedical Engineering and Informatics, Oct 16-18, 2010, Yantai, China 2010, 5: 1985-1987.
Uncertainty Quantification in Flight Plan Horizontal Path Using Evidence Theory, with Y. He, S. V. Poroseva and R. Mankbadi, Florida Center for Advanced Aeropropulsion (FCAAP) Annual Technical Symposium and Exhibition, Tallahassee, Florida, 9-10 August 2010.
Uncertainty Quantification in Flight Plans Using Evidence Theory: Departure and Arrival Times, with S. V. Poroseva, Yanyan He, J. J. Pesce and R. Mankbadi, 12th AIAA Non-Deterministic Approaches Conference, Orlando, Florida, USA, 12-15 April 2010.
Multimodel Approach Based on Evidence Theory for Forecasting Tropical Cyclone Tracks, with S.V. Poroseva and N. Lay, Monthly Weather Review, 2010, 138(2): 405-420.
A Systematic Study of Efficient Sampling Methods to Quantify Uncertainty in Crack Propagation and the Burgers Equation, with E. Jimenez and N. Lay, Monte Carlo Methods and Applications, 2010, 16(1): 69-93.
Asynchronous Time Integration for Polynomial Chaos Expansion of Uncertain Periodic Dynamics, with O. L. Maitre, L. Mathelin, O. Knio, Discrete and Continuous Dynamical Systems - A, 2010, 28 (1): 199-226.
Uncertainty Quantification in Flight Departure and Arrival Time Using Dempster-Shafer Theory of Evidence, with S. V. Poroseva and R. Mankbadi, Florida Center for Advanced Aeropropulsion (FCAAP) -- Annual Technical Symposium, Orlando, Florida, 13-14 August, 2009.
Quantifying Uncertainties in Aircraft Trajectories, with S. V. Poroseva and R. Mankbadi, 10th US National Congress On Computational Mechanics, Columbus, Ohio, July 16-19, 2009.
Stochastic Data Assimilation with A Karhunen-Loeve/Polynomial Chaos Statistical Reduction, with L. Mathelin, 8th World Congress on Computational Mechanics (WCCM8), 5th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008), June 30 July 5, 2008, Venice, Italy, 2008.
Quantification of Uncertainty Associated with Low-Fidelity Simulations, with G. Klopfer, P. Ngnepieba and A. Zatezalo, Proc. The Sixth International Conference on Engineering Computational Technology, Athens, Greece, 2-5 Septemebr 2008, edited by M. Papadrakakis and B. H. V. Topping, Civil-Comp Press, Stirlingshire, Scotland.
Quantifying Parametric Uncertainty in the Rothermel Model, with E. Jimenez and S. Goodrick, International Journal of Wildland Fire, 2008, 17(3): 638-649 .
Multiobjective Stochastic Control in Fluid Dynamics via Game Theory Approach. Application to Periodic Burgers Equation, with A.-M. Croicu, J. Optimization Theory and Applications, 2008, 138(2): 501-514.
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.
A variance reduction method based on sensitivity derivatives, with Y. Cao, T. A. Zang and A. Zatezalo, Applied Numerical Mathematics 2006, 56: 800-813.
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 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.