What question did this study set out to answer?

To develop a function-on-function Gaussian process (FFGP) for improving prediction uncertainty handling and robust parameter design.

Function-on-Function Gaussian Process with Application in Robust Parameter Design

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Novel algorithm shows improved robust parameter design in complex systems, suggesting better output uncertainty management.

To develop a function-on-function Gaussian process (FFGP) for improving prediction uncertainty handling and robust parameter design.
Proposed the function-on-function Gaussian process for modeling functional data directly.
Applied an expected loss function to measure functional output bias given a functional input.
Developed a functional gradient descent algorithm to identify optimal functional inputs.
FFGP can effectively model functional data in continuous space.
Demonstrated improvements in managing output uncertainty over existing models.

Huang et al. (2026) studied this question.

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