Xiu Yang

Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence.

2018 · arXiv (Cornell University)

In this work, we propose a new Gaussian process regression (GPR) method: physics-informed Kriging (PhIK). In the standard data-driven Kriging, the unknown function of interest is usually treated as a Gaussian process with assumed stationary …

Nonnegativity-enforced Gaussian process regression

2020 · Theoretical and Applied Mechanics Letters

Gaussian process (GP) regression is a flexible non-parametric approach to approximate complex models. In many cases, these models correspond to processes with bounded physical properties. Standard GP regression typically results in a proxy model which …

A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors

2020 · arXiv (Cornell University)

Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise which is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error …