most citedPAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

stat.ML2022

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification

Jiahao Zhang, Shiqi Zhang, Guang Lin

Multi-fidelity modelling arises in many situations in computational science and engineering world. It enables accurate inference even when only a small set of accurate data is avai…

stat.ML2022★ 1 cited

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

Jiahao Zhang, Shiqi Zhang, Guang Lin

In this work, a Gaussian process regression(GPR) model incorporated with given physical information in partial differential equations(PDEs) is developed: physics-assisted Gaussian…