Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Francesco Romor,
Marco Tezzele,
Gianluigi Rozza
Abstract:Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression embedding the inputs in a latent manifold inside an infinitedimensional reproducing kernel Hilbert space. We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus increment the model's accuracy. This can be realized recursively with a chain of Gaussian processes with incrementally higher fidelity. We would like to exten… Show more
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