2 papers
math.NA2019
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
Jing Li, Alexandre M Tartakovsky
We present a new approach for constructing a data-driven surrogate model and using it for Bayesian parameter estimation in partial differential equation (PDE) models. We first use…
stat.ML2018
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
Xiu Yang, Xueyu Zhu, Jing Li
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-mo…