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

Cheng Chang, Tieyong Zeng

Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data…