Researchers found that the Gaussian Process Regression (GPR) machine learning model is the most reliable tool for forecasting ...
Bayesian Optimization is a technique for efficiently maximizing (or minimizing) an objective function that is computationally expensive to evaluate. It models the function using a surrogate (a ...
The Annals of Probability, Vol. 1, No. 6 (Dec., 1973), pp. 968-981 (14 pages) This paper is mainly a survey of results on the problem of finding necessary and sufficient conditions for a Gaussian ...
This project covers comprehensive regression analysis including Linear Regression, Polynomial Regression, Model Selection, and Gaussian Process Regression with LIDAR data analysis. Advanced_ML_Project ...
Abstract: Accurately predicting crack growth is essential for maintenance and health management in engineering. The unpredictable nature of crack propagation in real structures requires leveraging ...
Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols Journal of the ...
Abstract: The residual stress inside a component is one of the main factors affecting its material property and manufacturing quality, such as geometric stability and fatigue life. It is important to ...
Sustainable Dyeing Process Modeling for Recycled PET/PCT Microfibers via Gaussian Process Regression
Department of Materials Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea Graduate School of Semiconductor Materials and Devices ...
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