IEEE

Efficient Electromagnetic Near-Field Scanning Using Physics-Informed Gaussian Process Regression

This paper proposes a novel approach combining prior physics-based Gaussian Process Regression (GPR) with Bayesian Optimization for efficient and accurate electromagnetic near-field scanning. By ...
IEEE

Incremental Heteroscedastic Gaussian Process Regression and Its Applications in Model Predictive Control

Abstract: Gaussian process regression (GPR) models are becoming increasingly tightly integrated into robotic systems, particularly in the context of robot model predictive control (MPC) operating in ...
C&EN

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
Scientific Research Publishing

Multi-Task Gaussian Process for Imputing Missing Daily Rainfall Data Using Nearby Stations: Case of Burkina Faso ()

1 Ecole Polytechnique de Ouagadougou, Ouagadougou, Burkina Faso. 2 Agence National Meteorologie, Ouagadougou, Burkina Faso. Precipitation is a critical meteorological factor that significantly impacts ...
Frontiers

Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems

Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG ...
Frontiers

Probabilistic net load forecasting based on sparse variational Gaussian process regression

1 State Grid Sichuan Information & Telecommunication Company, Chengdu, China 2 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China ...
GitHub

Gaussian Processes for Operator Learning

Code for the paper Operator Learning with Gaussian Processes, which introduces a general framework based on Gaussian Processes (GPs) for approximating single- or multi-output operators in either a ...