Results 111 to 120 of about 14,298 (230)

Transfer Learning-Enhanced Gaussian Process Regression (GPR) Model for Optimizing Stimulation Parameters with Limited Local Field Potential (LFP) Data

Journal of Physics: Conference Series
Abstract This study introduces transfer learning (TL) into rat deep brain stimulation (DBS) analgesia research to address the issue of data scarcity in the target domain. To this end, we propose an improved transfer component analysis (improved TCA) method that transfers source data to the target domain for constructing a Gaussian ...
Peijie Gao, Long Chen, Jing Wang, Jing Zhao   +3 more
openaire   +1 more source

Improving Basin‐Wide Flood Estimation From a Global Hydrological Model Through Spatiotemporal‐Pattern‐Based Machine Learning

Journal of Flood Risk Management, Volume 19, Issue 2, June 2026.
ABSTRACT When estimating future flood events using a global hydrological model (GHM), the large uncertainties associated with general circulation models (GCMs) and bias in the GHM model pose significant challenges. In the meantime, most future flood estimations are conducted only at specific gauge stations due to limited data availability and are ...
Jiaqing Wang, Quan J. Wang, Jianshi Zhao
wiley   +1 more source

Error Quantification of Gaussian Process Regression for Extracting Eulerian Velocity Fields from Ocean Drifters

Journal of Marine Science and Engineering
Drifter observations can provide high-resolution surface velocity data (Lagrangian data), commonly used to reconstruct Eulerian velocity fields. Gaussian Process Regression (GPR), a machine learning method based on Gaussian probability distributions, has
Junfei Xia, Mohamed Iskandarani, Rafael C. Gonçalves, Tamay Özgökmen   +3 more
doaj   +1 more source

Development of machine vision-based system for Iron ore grade prediction using gaussian process regression (GPR)

, 2016
India is one of the major iron ore producing country and requires quality monitoring of iron ore. An attempt has made to develop a vision-based system for continuous iron ore grade prediction during transportation of ores through conveyors. A Gaussian process regression (GPR) algorithm was used to develop the model.
Ashok Kumar Patel, Amit Kumar Gorai, Snehamoy Chatterjee   +2 more
openaire   +1 more source

Machine Learning‐Based Analysis of Atmospheric Process Sensitivity to Land Surface Properties Over Complex Terrain

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 9, 16 May 2026.
Abstract This study investigates the sensitivity of numerical simulations of atmospheric processes over complex terrain to land surface model (LSM) parameters, focusing on thermally driven circulations in an idealized valley. The sensitivity analysis is performed using the Weather Research and Forecasting model coupled with the Noah‐MP LSM, for forest ...
Dario Di Santo, Cenlin He, Fei Chen, Andrea Zonato, Lorenzo Giovannini   +4 more
wiley   +1 more source

Optimization of Flexural Strength of PLA Flexural Test Specimens Produced by FDM Using Taguchi L27 Orthogonal Array and Hybrid Regression Model

Journal of Polymer Science, Volume 64, Issue 10, Page 2122-2137, 15 May 2026.
This study investigates the impact of FDM process parameters on the flexural strength of PLA components using a Taguchi L27 orthogonal array. Analysis reveals wall thickness and layer height as the primary determinants of mechanical performance. A highly accurate regression model is developed, providing a robust predictive framework to optimize 3D ...
Mehmet Şah Gültekin, Cüneyt Özdemir
wiley   +1 more source

Advancements in evaporation prediction: introducing the Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR) model

Environmental Sciences Europe
Abstract Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still
Sharareh Pourebrahim, Mohammad Ehteram, Mehrdad Hadipour, Ozgur Kisi, Ahmed El-Shafie, Ali Najah Ahmed, Jit Ern Chen   +6 more
openaire   +2 more sources

BACH, a Bayesian Optimization Protocol for Accurate Coarse‐Grained Parameterization of Organic Liquids

Advanced Functional Materials, Volume 36, Issue 38, 11 May 2026.
We present a fully automated Bayesian optimization (BO) protocol for the parameterization of nonbonded interactions in coarse‐grain CG force fields (BACH). Using experimental thermophysical data, we apply the protocol to a broad range of liquids, spanning linear, branched, and unsaturated hydrocarbons, esters, triglycerides, and water.
Janak Prabhu, Akhil Pratap Singh, Pablo Campomanes, Stefano Vanni   +3 more
wiley   +1 more source

Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete

Computation
This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of
Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević, Miroslav Radomirović   +4 more
doaj   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, Volume 8, Issue 5, May 2026.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source