Gaussian Process Regression for Bayesian Fusion of Multi-Fidelity Information Sources

[1]  Peter Z. G. Qian,et al.  Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments , 2008, Technometrics.

[2]  W. D. Thomison,et al.  A Model Reification Approach to Fusing Information from Multifidelity Information Sources , 2017 .

[3]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[4]  Ilias Bilionis,et al.  Multi-output local Gaussian process regression: Applications to uncertainty quantification , 2012, J. Comput. Phys..

[5]  P Perdikaris,et al.  Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields , 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Michael Goldstein,et al.  Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations , 2009, Technometrics.

[7]  George E. Apostolakis,et al.  Including model uncertainty in risk-informed decision making , 2006 .

[8]  Karen Willcox,et al.  Surrogate Modeling for Uncertainty Assessment with Application to Aviation Environmental System Models , 2010 .

[9]  M. Drela XFOIL: An Analysis and Design System for Low Reynolds Number Airfoils , 1989 .

[10]  Loic Le Gratiet,et al.  Bayesian Analysis of Hierarchical Multifidelity Codes , 2011, SIAM/ASA J. Uncertain. Quantification.

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

[12]  Michael Goldstein,et al.  Constructing partial prior specifications for models of complex physical systems , 1998 .

[13]  Loic Le Gratiet,et al.  Multi-fidelity Gaussian process regression for computer experiments , 2013 .

[14]  Daniele Venturi,et al.  Multi-fidelity Gaussian process regression for prediction of random fields , 2017, J. Comput. Phys..

[15]  Douglas Allaire,et al.  Quantifying the Impact of Different Model Discrepancy Formulations in Coupled Multidisciplinary Systems , 2017 .

[16]  Alexander I. J. Forrester,et al.  Multi-fidelity optimization via surrogate modelling , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[17]  Samuel Friedman Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation , 2018 .

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

[19]  Ulisses Braga-Neto,et al.  Multiple Model Adaptive controller for Partially-Observed Boolean Dynamical Systems , 2017, 2017 American Control Conference (ACC).

[20]  Shishi Chen,et al.  Multimodel Fusion Based Sequential Optimization , 2017 .

[21]  A. O'Hagan,et al.  Predicting the output from a complex computer code when fast approximations are available , 2000 .

[22]  Loic Le Gratiet,et al.  RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY , 2012, 1210.0686.

[23]  Douglas Allaire,et al.  Compositional Uncertainty Analysis via Importance Weighted Gibbs Sampling for Coupled Multidisciplinary Systems , 2016 .

[24]  Ramana V. Grandhi,et al.  Quantification of Modeling Uncertainty in Aeroelastic Analyses , 2011 .

[25]  Ulisses Braga-Neto,et al.  ParticleFilters for Partially-ObservedBooleanDynamical Systems , 2017 .

[26]  Thomas D. Economon,et al.  Stanford University Unstructured (SU 2 ): An open-source integrated computational environment for multi-physics simulation and design , 2013 .

[27]  Ali Mosleh,et al.  The Assessment of Probability Distributions from Expert Opinions with an Application to Seismic Fragility Curves , 1986 .