Dr. Ming Ye
Department of Scientific Computing
Florida State University
PhD, University of Arizona
Office: 489 Dirac Science Library
Phone: (850) 644-4587
Fax: (850) 644-0098
Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Groundwater Reactive Transport Modeling
Graduate Student: Heng Dai
Subsurface environmental systems are open and complex, in which intricate biogeochemical processes interact across multiple spatial and temporal scales. Understanding and predicting system responses to natural forces and human activities is indispensable for environmental management and protection. However, predictions of the subsurface system are inherently uncertain and uncertainty is one of the greatest obstacles in groundwater reactive transport modeling. The goal of this project is two-fold: (1) developing new computational and mathematical methods for quantification of predictive uncertainty, and (2) using the developed methods as the basis to develop new methods of experimental design and data collection for reduction of predictive uncertainty. The proposed computational Bayesian framework is general and compatible with other widely used reactive transport models and numerical codes, so the advances can be easily applied to gain insights into subsurface biogeochemical processes that occur across a wide range of field sites and environmental conditions.
March 17, 2014, Dr. Ahmed Elshall joined our group as a post-doc working on this project.
March 11, 2014: Dr. Ye gave a presentation at the Illinois Geological Survey on our research of developing a Bayesian framework for uncertainty quantification.
February 14, 2014: Dr. Ye gave a presentation in the School of Geosciences at the University of Sourth Florida on our research of quantification of model uncertainty for groundwater reactive transport modeling.
December 9, 2013: Dr. Ye gave a presentation at the Lawrence Berkeley National Laboratory on our research of developing a Bayesian framework for uncertainty quantification.
Ye, M (2014), A Bayesian Framework for Uncertainty Quantification with Application to Groundwater Reactive Transport Modeling, SIAM Conference on Uncertainty Quantification, March 31 - April 3, Savannah, GA.
Ye, M., G. Zhang, D. Lu, and M. Gunzburger (2014), Using sparse grid methods for Bayesian uncertainty analysis in groundwater modeling, World Environmental & Water Resources Congress 2012, May 19 - 23, Cincinnati, OH.
Ye, M., L. Wang, P.Z. Lee, R.W. Hicks (2014), Uncertainty analysis for estimating nitrate loads from septic tanks to surface water bodies, World Environmental & Water Resources Congress 2012, May 19 - 23, Cincinnati, OH.
Ye, M., X. Shi, G.P. Curtis, M. Kohler, and J. Wu (2013), How to make data a blessing to parametric uncertainty quantification and reduction? AGU meeting, December 9 - 13, San Francisco, CA.
Curtis, G.P., M. Ye, D. Lu, Matthias Kohler, and R. Kannappan (2013), Evaluation of Uranium Transport Prediction Uncertainty in Field Experiments, AGU meeting, December 9 - 13, San Francisco, CA.
Dai, H. and M. Ye (2013), Combined Estimation of Hydrogeologic Scenario, Model, and Parameter Uncertainty and Sensitivity with Application to Groundwater Reactive Transport Modeling, AGU meeting, December 9 - 13, San Francisco, CA.
Liu, P., M. Ye, D. Lu, Y. Tao, and M. Shang (2013), Effects of spatial correlation of model errors on calculation of model averaging weights, AGU meeting, December 9 - 13, San Francisco, CA.
Zhang,G., D. Lu, M. Ye, M. Gunzburger (2012), An efficient sparse-grid-based Bayesian method for uncertainty analysis in groundwater reactive transport modeling, Annual Meeting of the American Geophysical Union, December 3-7, San Francisco, CA.
Dai, H., M. Convertino, I. Linkov, M. Ye, and Z. Collier (2012), A Bayesian network approach for ecogeological modeling facing uncertainty, Annual meeting of the Geological Society of America, November 3 - 7, 2012, Charlotte, NC.
Zhang G.(Student), D. Lu (Student), M. Ye, M. Gunzburger, and C. Webster (2013), An efficient surrogate modeling approach in Bayesian uncertainty analysis, 11th International Conference of Numerical Analysis and Applied Mathematics, September 21-27, Rhodes, Greece.
Peer-Reviewed Journal Articles:
Zhang, G. (student), D. Lu (student), M. Ye, M. Gunzburger, and C. Webster (2013), An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling, Water Resour. Res., 49, doi:10.1002/wrcr.20467.
Lu, D. (student), M. Ye, P.D. Meyer, G.P. Curtis, X. Shi, X.-F. Niu, and S.B. Yabusaki (2013), Effects of error covariance structure on estimation of model averaging weights and predictive performance, Water Resour. Res., 49, doi:10.1002/wrcr.20441.
Lu, D. (student), M. Ye, and M.C. Hill (2012), Analysis of Regression Confidence Intervals and Bayesian Credible Intervals for Uncertainty Quantification, Water Resources Research,48, W09521, doi:10.1029/2011WR011289.
Gupta, H.V., M.P. Clark, J.A. Vrugt, G. Abramowitz, and M. Ye (2012), Towards a comprehensive assessment of model structural adequacy, Water Resources Research, 48, W08301, doi:10.1029/2011WR011044.