Results 31 to 40 of about 4,489 (189)
A Gaussian process regression (GPR) quest to predict HOMO-LUMO energy
, 2023 Machine learning methods employ statistical algorithms and pattern recognition techniques to learn patterns and make predictions based on statistical patterns. Global reactivity descriptors, such as HOMO-LUMO energy, chemical potential (µ), chemical hardness (η), softness (σ) and electrophilic index (ω) are predicted using Gaussian process regression ...
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Neural Computing and Applications, 2022 Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources.
Humphrey Adun +8 more
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Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors
Sensors, 2022 A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen.
Xiaoyu Song +5 more
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3C Tecnología_Glosas de innovación aplicadas a la pyme, 2023 Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow.
Ruya mehdi, Ayse Yeter GUNAL
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Scientific Reports, 2022 In view of the shortcomings of existing artificial neural network (ANN) and support vector regression (SVR) in the application of three-dimensional displacement back analysis, Gaussian process regression (GPR) algorithm is introduced to make up for the ...
Hanghang Yan +3 more
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Engine Emission Prediction Based on Extrapolated Gaussian Process Regression Method
Shanghai Jiaotong Daxue xuebao, 2022 Aimed at improving the prediction accuracy of engine emissions under driving conditions which are not covered by the training set, an extrapolated Gaussian process regression (GPR) method is proposed.
WANG Ziyao, GUO Fengxiang, CHEN Li
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State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model
IEEE Access, 2019 State-of-health (SOH) prediction for lithium-ion batteries is a challenging and important topic in the modern industry. With the advent of cloud-connected devices, there are huge amounts of the battery degradation trend data available.
Xueying Zheng, Xiaogang Deng
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Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Tunnel geomechanical parameters prediction using Gaussian process regression
Machine Learning with Applications, 2021 The purpose of this study is to apply a modern intelligent method of Gaussian process regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) along the tunnel route. This method can also be used for any geological parameter
Arsalan Mahmoodzadeh +6 more
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Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source