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We propose a data fusion method based on multi-fidelity Gaussian process regression (GPR) framework.
Universal kriging and cokriging as a regression procedure
A Stein and LCA Corsten · 1991
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Simulation of moisture deficits and areal interpolation by universal cokriging
A Stein, IG Staritsky, J Bouma, AC Van Eijnsbergen, and AK Bregt · 1991
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Bayesian design and analysis of computer experiments: use of derivatives in surface prediction
Max D Morris, Toby J Mitchell, and Donald Ylvisaker · 1993
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Power systems test case archive, May 1993
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Statistical mechanics of dissipative particle dynamics
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A review of gaussian random fields and correlation functions, 1997
Petter Abrahamsen · 1997
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Introduction to Geostatistics: Applications in Hydrogeology
Peter K Kitanidis · 1997
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Coarse-grained molecular dynamics and the atomic limit of finite elements
Robert E Rudd and Jeremy Q Broughton · 1998
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Predicting the output from a complex computer code when fast approximations are available
Marc C Kennedy and Anthony O’Hagan · 2000
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Design of a low-boom supersonic business jet using cokriging approximation models
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Gaussian processes for machine learning
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Engineering Design via Surrogate Modelling: A Practical Guide
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Giancarlo Alfonsi · 2009
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Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields
P Perdikaris, D Venturi, JO Royset, and GE Karniadakis · 2015
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Performance study of multi-fidelity gradient enhanced kriging
Selvakumar Ulaganathan, Ivo Couckuyt, Francesco Ferranti, Eric Laermans, and Tom Dhaene · 2015
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A general cfd framework for fault-resilient simulations based on multi-resolution information fusion
Seungjoon Lee, Ioannis G Kevrekidis, and George Em Karniadakis · 2017
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
Paris Perdikaris, Maziar Raissi, Andreas Damianou, ND Lawrence, and George Em Karniadakis · 2017
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Multi-fidelity machine learning models for accurate bandgap predictions of solids
Ghanshyam Pilania, James E Gubernatis, and Turab Lookman · 2017
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Comparison of gradient-based and gradient-enhanced response-surface-based optimizers
J Laurenceau, M Meaux, M Montagnac, and P Sagaut · 2010
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Matpower: Steady-state operations, planning, and analysis tools for power systems research and education
Ray Daniel Zimmerman, Carlos Edmundo Murillo-Sánchez, and Robert John Thomas · 2011
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Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function
Zhong-Hua Han, Stefan Görtz, and Ralf Zimmermann · 2013
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On the maximum likelihood training of gradient-enhanced spatial gaussian processes
Ralf Zimmermann · 2013
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3d inversion of airborne gravity-gradiometry data using cokriging
Meixia Geng, Danian Huang, Qingjie Yang, and Yinping Liu · 2014
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Recursive co-kriging model for design of computer experiments with multiple levels of fidelity
Loic Le Gratiet and Josselin Garnier · 2014
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Survey of multifidelity methods in uncertainty propagation, inference, and optimization
Benjamin Peherstorfer, Karen Willcox, and Max Gunzburger · 2018
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Xiu Yang, Guzel Tartakovsky, and Alexandre Tartakovsky · 2018
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An overview of gradient-enhanced metamodels with applications
Luc Laurent, Rodolphe Le Riche, Bruno Soulier, and Pierre-Alain Boucard · 2019
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Linking gaussian process regression with data-driven manifold embeddings for nonlinear data fusion
Seungjoon Lee, Felix Dietrich, George E Karniadakis, and Ioannis G Kevrekidis · 2019
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Physics-informed cokriging: A gaussian-process-regression-based multifidelity method for data-model convergence
Xiu Yang, David Barajas-Solano, Guzel Tartakovsky, and Alexandre M Tartakovsky · 2019
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A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems
Xuhui Meng and George Em Karniadakis · 2020
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When bifidelity meets cokriging: An efficient physics-informed multifidelity method
Xiu Yang, Xueyu Zhu, and Jing Li · 2020
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