Scientific Research Publishing

A New Method to Predict the Mechanical Behavior for a Family of Composite Materials ()

Mînzu, V. and Arama, I. (2025) A New Method to Predict the Mechanical Behavior for a Family of Composite Materials. Journal ...
C&EN

Data-Driven Design of High-Temperature-Resistant Polyimides Using Hierarchical Gaussian Process Regression

School of Materials Science and Engineering, Beihang University, Beijing 100191, China State Key Laboratory of Artificial Intelligence for Materials Science, Beihang University, Beijing 100091, China ...
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 ...
IEEE

Implementation of a Real-Time Force Estimation System Based on sEMG Signals and Gaussian Process Regression: Human–Robot Interaction in Rehabilitation

Abstract: Human force estimation has numerous applications, including biomedical models, rehabilitation, biomechanical system control, and human-machine interfaces. To enable such applications, it is ...
IEEE

Forecasting of Wind Power Using Modified Exponential Gaussian Process Regression

Abstract: Wind power is becoming increasingly crucial as a renewable power source in the present day. However, due to the unpredictable nature of wind, accurate power forecasting is essential for a ...
GitHub

spbridge: Marginally interpretable spatial logistic regression with bridge processes

Consider the following logistic mixed model with a spatial random effect for spatially indexed binary data: $$ \mathrm{logit}{\mathrm{Pr}(Y_{ij} = 1 \mid x_{ij}, u(s ...