A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.
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An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.
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Multifidelity Gaussian process regression for solving nonlinear partial differential equations
A multifidelity cokriging kernel-learning approach is proposed to construct high-fidelity kernels and means for Gaussian process solutions of nonlinear PDEs, demonstrated on Burgers' equation.
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An ensemble-based approach for multi-fidelity emulation and adaptive sampling
An ensemble of hierarchical kriging emulators aggregated by Bayesian model averaging yields accurate multi-fidelity predictions with uncertainty-driven adaptive sampling that outperforms single models on benchmarks.