Typically, GPR performs well for nonlinear regression problems with small datasets . The prediction is probabilistic and can simultaneously predict the output value and the associated uncertainty intervals , as depicted in Fig. (f). However, the evaluation of the multivariate Gaussian density in the maximum likelihood estimation for GPR requires computing the determinant and inversion of the covariance matrix. These operations, with cubic computational complexity, impose significant computational costs, making GPR inefficient for large datasets.