Analyzing the effects of various isotropic and anisotropic kernels on critical heat flux prediction using Gaussian process regression
M Soleimani, M Esmaeilbeigi, R Cavoretto… - … Applications of Artificial …, 2024 - Elsevier
The critical heat flux (CHF) is an important parameter determining the heat transfer capability
of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is …
of nuclear reactors. Therefore, prediction of CHF with accuracy and correct understanding is …
Novel approaches for hyper-parameter tuning of physics-informed Gaussian processes: application to parametric PDEs
M Ezati, M Esmaeilbeigi, A Kamandi - Engineering with Computers, 2024 - Springer
Today, Physics-informed machine learning (PIML) methods are one of the effective tools
with high flexibility for solving inverse problems and operational equations. Among these …
with high flexibility for solving inverse problems and operational equations. Among these …
An active-subspace-enhanced support vector regression model for high-dimensional uncertainty quantification
Y Zhou, X Gong, X Zhang - 2024 - researchsquare.com
The computational costs of surrogate model-assisted uncertainty quantification methods
become intractable for high dimensional problems. However, many high-dimensional …
become intractable for high dimensional problems. However, many high-dimensional …