The authors extend the design of experiments framework for multi-fidelity Gaussian processes by partitioning prediction uncertainty according to fidelity and cost and incorporating the Believer concept for adaptive sampling, demonstrated on academic examples and a fluidized bed industrial case.
Multi-fidelity surrogate modeling for application/architecture co-design
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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
The authors extend the design of experiments framework for multi-fidelity Gaussian processes by partitioning prediction uncertainty according to fidelity and cost and incorporating the Believer concept for adaptive sampling, demonstrated on academic examples and a fluidized bed industrial case.