Results 71 to 80 of about 14,298 (230)

Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics

Scientific Reports
A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression
Heyder Mhohamdi, Usama S. Altimari, Krunal Vaghela, V. Vivek, Sarbeswara Hota, Devendra Singh, Mahesh Manchanda, Shirin Shomurotova, Prakhar Tomar, Mohammad Mahtab Alam   +9 more
doaj   +1 more source

Boosting battery health prediction in electric vehicles via extended quantum optimized model with gaussian process regression

Next Materials
Accurate forecasting of battery state of health is essential for ensuring the reliability of electric vehicles. Conventional Quantum Particle Swarm Optimized Neural Network struggles with capturing non-linear temporal dependencies in battery degradation ...
Kian Lun Soon, Lam Tatt Soon, Nai Shyan Lai, Raveendran Paramesran, Wen Xun Lian, Chee Yong Lau   +5 more
doaj   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Fingerprinting-Based Positioning in Distributed Massive MIMO Systems

, 2015
Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-
Larsson, Erik G., Savic, Vladimir
core   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Gaussian Process Regression (GPR)-based missing data imputation and its uses for bridge structural health monitoring

Advances in Bridge Engineering
Abstract Structural health monitoring (SHM) apparatuses rely on continuous measurement and analysis to assess the safety condition of a target system. However, in field applications, the SHM framework is often hampered by practical issues.
Dalmasso, Matteo, Civera, Marco, De Biagi, Valerio, Chiaia, Bernardino   +3 more
openaire   +3 more sources

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Optimizing aerodynamic shape of benchmark problems using an improved Gaussian process regression algorithm

Engineering Applications of Computational Fluid Mechanics
The current challenges encountered in Surrogate-Based Optimization (SBO) primarily stem from the substantial number of function calls essential for accurate evaluations.
Youtao Xue, Yuxin Yang, Shaobo Yao, Wenwen Zhao, Lihua Chen   +4 more
doaj   +1 more source

Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops

Ecological Processes, 2022
Background The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located ...
Terenzio Zenone, Luca Vitale, Daniela Famulari, Vincenzo Magliulo   +3 more
doaj   +1 more source

Statistical Gravitational Waveform Models: What to Simulate Next?

, 2017
Models of gravitational waveforms play a critical role in detecting and characterizing the gravitational waves (GWs) from compact binary coalescences. Waveforms from numerical relativity (NR), while highly accurate, are too computationally expensive to ...
Doctor, Zoheyr, Farr, Ben, Holz, Daniel E., Pürrer, Michael   +3 more
core   +1 more source