Polyethylene glycol-nano composite thermal conductivity: Introduction of data-driven models

Abstract The thermal conductivity of polyethylene glycol (PEG)-nanocomposites is crucial for their use in thermal management systems, underscoring the need for accurate predictive models. This study develops a Gradient Boosting Machine (GBM) model optimized with four algorithms: Batch Bayesian Optim...

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Bibliografski detalji

Glavni autori:	ZAHRAA SABAH GHNIM, AYAT HUSSEIN ADHAB, ASHA RAJIV, ANUPAM YADAV, HAIDER RADHI SAUD, AMAN SHANKHYAN, SACHIN JAIDKA, KAMAL KANT JOSHI, MORUG SALIH MAHDI, ASEEL SALAH MANSOOR, USAMA KADEM RADI, NASR SAADOUN ABD, MEHRDAD MOTTAGHI
Format:	Artigo
Jezik:	Inglês
Izdano:	Academia Brasileira de Ciências 2026-01-01
Serija:	Anais da Academia Brasileira de Ciências
Teme:	Machine learning-based modeling Thermal conductivity of polyethylene glycol (PEG)-nano composites Advanced predictive system Gradient Boosting Machine Optimization techniques
Online pristup:	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652025000400508&lng=en&tlng=en
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