Uncertainty and Computer Models

With the explosion of computing power in recent years, computer models have become important tools for scientists to study the behavior of different phenomena as well as for prediction and decision making. However, there are many sources of uncertainty in computer models that can have significant impact on what is learned from such models. The field of uncertainty quantification, which combines elements from statistics, applied mathematics, and computational science, is emerging to provide tools to better understand, quantify, and mitigate the many uncertainties in computer models, and this article covers some of the key issues and problems facing researchers in this area. Keywords: forward problem; inverse problem; Gaussian process emulator; multimodel ensembles; climate model

[1]  D. Higdon,et al.  Computer Model Calibration Using High-Dimensional Output , 2008 .

[2]  Stephan R. Sain,et al.  A spatial analysis of multivariate output from regional climate models , 2011, 1104.2703.

[3]  Richard G. Jones,et al.  A Regional Climate Change Assessment Program for North America , 2009 .

[4]  T. J. Mitchell,et al.  Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments , 1991 .

[5]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[6]  K. Taylor,et al.  An overview of results from the Coupled Model Intercomparison Project , 2003 .

[7]  Leonard A. Smith,et al.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.

[8]  George Michailidis,et al.  Sequential Experiment Design for Contour Estimation From Complex Computer Codes , 2008, Technometrics.

[9]  Noel A. C. Cressie,et al.  Statistics for Spatial Data: Cressie/Statistics , 1993 .

[10]  Randy R. Sitter,et al.  A new and flexible method for constructing designs for computer experiments , 2010, 1010.0328.

[11]  James O. Berger,et al.  A Framework for Validation of Computer Models , 2007, Technometrics.

[12]  Reto Knutti,et al.  Challenges in Combining Projections from Multiple Climate Models , 2010 .

[13]  S. Sain,et al.  Bayesian functional ANOVA modeling using Gaussian process prior distributions , 2010 .

[14]  Ann Henderson-Sellers,et al.  A Climate Modelling Primer: McGuffie/A Climate Modelling Primer , 2005 .

[15]  J. Rougier.  Efficient Emulators for Multivariate Deterministic Functions , 2008 .

[16]  D. Xiu,et al.  Modeling Uncertainty in Steady State Diffusion Problems via Generalized Polynomial Chaos , 2002 .

[17]  James O. Berger,et al.  Modularization in Bayesian analysis, with emphasis on analysis of computer models , 2009 .

[18]  Stephan R. Sain,et al.  Functional ANOVA and regional climate experiments: a statistical analysis of dynamic downscaling , 2011 .

[19]  Richard D. Wilkinson,et al.  Bayesian Calibration of Expensive Multivariate Computer Experiments , 2010 .

[20]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  A. O'Hagan,et al.  Probabilistic sensitivity analysis of complex models: a Bayesian approach , 2004 .

[22]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[23]  James V. Zidek,et al.  Monitoring Network Design , 2010 .

[24]  Stephan R. Sain,et al.  Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model , 2008 .