A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by …
Gaussian process regression (GPR) is an immensely popular choice for computational scientists as a surrogate model for various uncertainty quantification tasks. However, it's appliacability is limited by it's poor scalability to high input …