Date of Award
5-1-2026
Document Type
Thesis
Degree Name
M.S. in Engineering Science
First Advisor
Dr. Farhad Farzbod
Second Advisor
Dr. Yiwie Han
Third Advisor
Dr. Ohood Alsmairat
School
University of Mississippi
Relational Format
dissertation/thesis
Abstract
Periodic structures are central to the design of phononic metamaterials, where unit cell configurations determine vibration attenuation and frequency-selective behavior. While many analyses assume interactions are limited to near neighbors, systems governed by long-range forces such as electrostatic coupling exhibit non-local interactions that modify the resulting dispersion manifold. Although Bloch-based formulations accurately resolve these curves, key features like bandgap count and branch-wise energy distribution lack closed-form expressions, often necessitating computationally intensive spectral scanning for parametric studies. To address this, we present a data driven machine learning framework to estimate these characteristics directly from structural geometry. By introducing the per-branch Area Under the Curve as a predictive target alongside bandgap count, the model captures the integrated spectral weight and group velocity characteristics of the system. A hybrid modeling approach, utilizing Random Forest and Gradient Boosting architectures, was developed to map the relationship between geometric parameters and these spectral features. The resulting surrogate achieves 92.15% accuracy in predicting bandgap count and an average R2 of 0.9719 for branch-wise AUC. These results indicate that the framework can serve as an efficient framework for evaluating non-local metamaterial designs, providing a computationally accessible alternative to traditional wavenumber sweeping in long-range coupled system.
Recommended Citation
Thapa, Anisha, "Data-driven Prediction of Bandgaps in Periodic Structures with Beyond Nearest Neighbor Interactions" (2026). Electronic Theses and Dissertations. 8878.
https://egrove.olemiss.edu/etd/8878