Results 61 to 70 of about 10,881 (265)

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Signal processing method in GPR

, 2021
GPR ' de Altuzay Karmaşası Giderme Teknikleri Birçok GPR sisteminde parazit ve gürültü içeren girişimlerin varlığının saptanması arzulanır. Gömülü nesnelerden elde edilen yansıma sinyalleri, esas olarak yer altı homojen olmamalarından, düz veye pürüzlü zemin yüzeylerinden ve verci alıcı antenler arasında bağantı görevi gören kuvvetli dağınıklık ...
openaire   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +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

Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms

Renmin Zhujiang, 2023
Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and ...
WANG Yongshun, CUI Dongwen
doaj  

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

Adaptive reliability analysis of spatially variable soil slopes using strength reduction sampling and Gaussian process regression

Yantu gongcheng xuebao
Spatial variability of soil parameters has significant impact on slope stability. The critical problem for the slope reliability analysis considering the spatial variability is the dramatic computational demand.
LIU Yadong 1, 3, LIU Xian 1, LI Xueyou 1, 2, YANG Zhiyong 1
doaj   +1 more source

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

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures. [PDF]

J Appl Clin Med Phys
Abstract Background Patient‐specific quality assurance (PSQA) plays a pivotal role in intensity‐modulated radiotherapy (IMRT) to ensure accurate dose delivery. However, conventional measurement‐based PSQA approaches are labor‐intensive and provide limited insight into the underlying factors contributing to variations in gamma passing rates (GPRs ...
Zhang X, Huang Y, Wang J, Zhang X, Chen H, Luo Y, Xie J, Xu Z, Xu Y.   +8 more
europepmc   +2 more sources