5 citations · 9 across the 5 of their papers we have counts for
7 papers
A non-intrusive bi-fidelity reduced basis method for time-independent problems
Jun Sur Richard Park, Xueyu Zhu
Scientific and engineering problems often involve parametric partial differential equations (PDEs), such as uncertainty quantification, optimizations, and inverse problems. However…
Forward and inverse problems for Eikonal equation based on DeepONet
Yifan Mei, Yijie Zhang, Xueyu Zhu +1
Seismic forward and inverse problems are significant research areas in geophysics. However, the time burden of traditional numerical methods hinders their applications in scenarios…
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion
Andrew Pensoneault, Xueyu Zhu
Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on par…
Nonnegativity-Enforced Gaussian Process Regression
Andrew Pensoneault, Xiu Yang, Xueyu Zhu
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 prop…
A bi-fidelity method for the multiscale Boltzmann equation with random parameters
Liu Liu, Xueyu Zhu
In this paper, we study the multiscale Boltzmann equation with multi-dimensional random parameters by a bi-fidelity stochastic collocation (SC) method developed in [A. Narayan, C.…
Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
Chuan Lu, Xueyu Zhu
In this paper, we present a new nonintrusive reduced basis method when a cheap low-fidelity model and expensive high-fidelity model are available. The method relies on proper ortho…