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Chang et al., 2023 - Google Patents

A hybrid data-driven-physics-constrained Gaussian process regression framework with deep kernel for uncertainty quantification

Chang et al., 2023

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Document ID
13038387483245851143
Author
Chang C
Zeng T
Publication year
Publication venue
Journal of Computational Physics

External Links

Snippet

Gaussian process regression (GPR) is a well-known machine learning method employed for various applications such as uncertainty quantifications. However, GPR is an inherently data- driven method that requires a sufficiently large dataset. If appropriate physics constraints (eg …
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Classifications

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