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 Seema; Singhal, Abhinav Title A name given to the resource Hybrid Analytical-Machine Learning Framework for SH-Wave Dispersion in Piezo-Flexoelectric Layered Structures with Imperfect Interfaces 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 Journal of Engineering Mechanics;Volume;152;Issue;6;Article No.;4026020; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1061/JENMDT.EMENG-8884" target="_blank" rel="noreferrer noopener">https://doi.org/10.1061/JENMDT.EMENG-8884</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035391118?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035391118?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 Seema, Dept. of Mathematics, Christ Univ., Bengaluru, Karnataka, 560029, India; Singhal A., Dept. of Mathematics, Christ Univ., Bengaluru, Karnataka, 560029, India Description An account of the resource This study presents a hybrid analytical-machine learning (ML) framework for modeling shear-horizontal (SH) wave propagation in piezo-flexoelectric (PFE) layered structures with imperfect interfacial bonding. The governing dispersion relations were derived using mechanical, electrical, and flexoelectric continuity conditions, establishing explicit links among phase velocity, wave number, layer thickness ratio, and flexoelectric coefficients. Analytical results revealed that nanoscale flexoelectricity and interfacial imperfections significantly influence wave behavior: electrically open (EO) boundaries amplify electromechanical coupling and enhance dispersion, whereas electrically shorted (ES) conditions suppress these effects, leading to reduced phase velocities. To address the computational cost associated with evaluating high-dimensional parametric spaces, ML surrogate models were incorporated. Convolutional neural network (CNN) and k-nearest neighbor (KNN) regressors accurately reproduced analytical dispersion surfaces with errors below 3% and drastically reduced computation time by more than two orders of magnitude. Classification models provided &gt;90% accuracy in distinguishing EO and ES boundary-condition regimes, and generative artificial intelligence (AI) variational autoencoder/generative adversarial network (VAE/GAN) architectures successfully synthesized dispersion surfaces for previously unseen flexoelectric parameter ranges. The proposed hybrid framework combines the physical rigor of analytical modeling with the efficiency of modern ML surrogates, enabling rapid parametric exploration and high-fidelity prediction of SH-wave characteristics. The outcomes support accelerated design of advanced surface acoustic wave (SAW) devices, micro-electro-mechanical systems (MEMS) components, and nanoscale electromechanical systems. Future work will extend the approach to experimental validation and uncertainty-aware modeling for real-world applications. 2026 American Society of Civil Engineers. Subject The topic of the resource Imperfect interfaces; Machine learning surrogates; Physics-informed neural networks; Piezo-flexoelectricity; Shear-horizontal waves; Surface acoustic wave (SAW)/micro-electro-mechanical systems (MEMS) applications Publisher An entity responsible for making the resource available American Society of Civil Engineers (ASCE) Relation A related resource ISSN: 7339399; 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