Reduced order models for many-query subsurface flow applications

[1]  David Higdon,et al.  Adaptive Hessian-Based Nonstationary Gaussian Process Response Surface Method for Probability Density Approximation with Application to Bayesian Solution of Large-Scale Inverse Problems , 2012, SIAM J. Sci. Comput..

[2]  Bryan A. Tolson,et al.  Review of surrogate modeling in water resources , 2012 .

[3]  Bryan A. Tolson,et al.  Numerical assessment of metamodelling strategies in computationally intensive optimization , 2012, Environ. Model. Softw..

[4]  Neil D. Lawrence,et al.  Kernels for Vector-Valued Functions: a Review , 2011, Found. Trends Mach. Learn..

[5]  Rodolphe Le Riche,et al.  Parallel Expected Improvements for Global Optimization: Summary, Bounds and Speed-up , 2011 .

[6]  Jeremy Rohmer,et al.  Global sensitivity analysis of large-scale numerical landslide models based on Gaussian-Process meta-modeling , 2011, Comput. Geosci..

[7]  Jérémy Rohmer,et al.  Global sensitivity analysis of large-scale numerical , 2011 .

[8]  Sankaran Mahadevan,et al.  BIAS MINIMIZATION IN GAUSSIAN PROCESS SURROGATE MODELING FOR UNCERTAINTY QUANTIFICATION , 2011 .

[9]  Karen Willcox,et al.  Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems , 2010, SIAM J. Sci. Comput..

[10]  A. O'Hagan,et al.  Bayesian emulation of complex multi-output and dynamic computer models , 2010 .

[11]  Carl E. Rasmussen,et al.  Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..

[12]  Bertrand Iooss,et al.  Numerical studies of the metamodel fitting and validation processes , 2010, 1001.1049.

[13]  Bertrand Iooss,et al.  Global sensitivity analysis for models with spatially dependent outputs , 2009, 0911.1189.

[14]  Louis J. Durlofsky,et al.  Development and application of reduced‐order modeling procedures for subsurface flow simulation , 2009 .

[15]  Robert B. Gramacy,et al.  Adaptive Design and Analysis of Supercomputer Experiments , 2008, Technometrics.

[16]  Olivier Roustant,et al.  Calculations of Sobol indices for the Gaussian process metamodel , 2008, Reliab. Eng. Syst. Saf..

[17]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[18]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[19]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[20]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[21]  Dorin Drignei,et al.  PARAMETER ESTIMATION FOR COMPUTATIONALLY INTENSIVE NONLINEAR REGRESSION WITH AN APPLICATION TO CLIMATE MODELING , 2008, 0901.3665.

[22]  M. Eldred,et al.  Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions , 2008 .

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

[24]  S. Finsterle,et al.  Advanced Vadose Zone Simulations Using TOUGH , 2008 .

[25]  A. J. Dentsoras,et al.  Soft computing in engineering design - A review , 2008, Adv. Eng. Informatics.

[26]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[27]  Bertrand Iooss,et al.  An efficient methodology for modeling complex computer codes with Gaussian processes , 2008, Comput. Stat. Data Anal..

[28]  M. J. Bayarri,et al.  Computer model validation with functional output , 2007, 0711.3271.

[29]  A. Patera,et al.  Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations , 2007 .

[30]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[31]  P. Beran,et al.  Reduced-order modeling: new approaches for computational physics , 2004 .

[32]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[33]  Mark J. Schervish,et al.  Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.

[34]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[35]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[36]  D. Rovas,et al.  Reliable Real-Time Solution of Parametrized Partial Differential Equations: Reduced-Basis Output Bound Methods , 2002 .

[37]  Richard K. Beatson,et al.  Reconstruction and representation of 3D objects with radial basis functions , 2001, SIGGRAPH.

[38]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[39]  K. Pruess,et al.  TOUGH2 User's Guide Version 2 , 1999 .

[40]  I. Sobol,et al.  Sensitivity Measures, ANOVA-like Techniques and the Use of Bootstrap , 1997 .

[41]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[42]  S. Finsterle,et al.  Solving the Estimation‐Identification Problem in Two‐Phase Flow Modeling , 1995 .

[43]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[44]  J. -F. M. Barthelemy,et al.  Approximation concepts for optimum structural design — a review , 1993 .

[45]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[46]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[47]  L. A. Schmit,et al.  Some approximation concepts for structural synthesis , 1973 .