ML-Surrogate Model

<p dir="ltr">This repository contains attachments related to the publication:</p><p dir="ltr">**[<b>Machine Learning Surrogate Optimization Framework for an Additively Manufactured, Linearly Polarized Metal Coaxial-to-Circular Waveguide Transition for Space Applications</b>] **</p><p dir="ltr">**Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or DOI <a href="https://doi.org/10.1080/xxxyyyzzzz" target="_blank">https://doi.org/10.1080/xxxyyyzzzz</a>]</p><p dir="ltr"><u>## Description</u></p><p dir="ltr">This repository provides access to additional resources that enhance or support the findings presented in the main publication. These materials may include,</p><p dir="ltr">* **Datasets: ** The raw or processed datasets used for the analysis in the publication.</p><p dir="ltr">***Surrogate Model: ** Models generated</p><p dir="ltr"><u>## Contents</u></p><p dir="ltr">1-Folder Dataset & Models ----------------------This folder has the Dataset and Trained Models</p><p dir="ltr">## How to Use</p><p dir="ltr">The files in this repository are organized. You can browse the contents and download individual files as needed.</p><p dir="ltr">To access the main publication, please refer to: the publication's DOI link https://doi.org/10.1080/xxxyyyzzzz].</p><p dir="ltr">## License</p><p dir="ltr">This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/</p><p>.</p><p dir="ltr">## Citation</p><p dir="ltr">If you use the data or materials in this repository, please cite the original publication. And if you specifically use any of the attached files, you may also consider referencing the repository.</p><p dir="ltr">## Contact</p><p dir="ltr">For any questions or inquiries regarding this repository or the publication, please contact:</p><p><br></p><p dir="ltr">**Last Updated:** December 14, 2025</p>