Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singhal, Abhinav; Abass, Kasim Sakran; Hassaballa, Abaker A.; Ahmed, Nada Title A name given to the resource Fractional MooreGibsonThomson thermoelastic analysis of nonlocal nanobeams under moving heat source with machine learning-assisted predictive modeling Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Communications in Heat and Mass Transfer;Volume;175;Issue;P3;Article No.;111154; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.icheatmasstransfer.2026.111154" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icheatmasstransfer.2026.111154</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034579207?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034579207?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singhal A., Christ University, Bengaluru, 560029, India; Abass K.S., University of Kirkuk, Kirkuk, 36001, Iraq; Hassaballa A.A., Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, 73213, Saudi Arabia; Ahmed N., Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia Description An account of the resource This study presents a comprehensive investigation of thermoelastic wave propagation in nonlocal nanobeams subjected to a moving heat source within the framework of fractional MooreGibsonThomson (MGT) heat conduction theory. The model incorporates nonlocal elasticity to capture size-dependent mechanical behavior and employs a fractional-order formulation to account for thermal memory and finite-speed heat propagation. The coupled governing equations are derived and solved analytically using Laplace transform techniques to obtain the temperature, displacement, and stress distributions. A detailed parametric analysis is performed to examine the effects of fractional order, nonlocal parameter, thermal relaxation time, and source velocity on the thermoelastic response. The results reveal significant modifications in wave attenuation, temperature evolution, and stress distribution due to the combined influence of nonlocality and fractional thermal effects, particularly under moving thermal loads. To enhance computational efficiency and enable rapid prediction of system responses, a machine learning-based surrogate framework is developed using an artificial neural network (ANN). The network is trained on data generated from the present analytical model and is shown to accurately predict thermoelastic fields across a wide range of governing parameters. The ANN predictions exhibit excellent agreement with analytical results, demonstrating its capability as a reliable reduced-order modeling tool. The proposed hybrid analyticalcomputational approach provides new insights into thermoelastic behavior at the nanoscale and offers an efficient predictive framework for heat transfer applications involving moving thermal loads. This study is motivated by the need to address unresolved challenges in modeling thermoelastic behavior at the nanoscale, particularly the simultaneous incorporation of fractional heat conduction, nonlocal elasticity, and moving thermal loads within a unified framework. 2026 Published by Elsevier Ltd. Subject The topic of the resource Artificial neural network; Fractional MooreGibsonThomson model; Moving heat source; Nanobeam heat transfer; Nanoscale thermal waves; Nonlocal thermoelasticity; Surrogate modeling Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 7351933; CODEN: IHMTD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online