ML-Surrogate Model

This repository contains attachments related to the publication:

**[Design of a Metal 3D-Printed, Linearly Polarized Coaxial-to-Circular Waveguide Transition for High-Power Microwave Space Applications Using a Machine Learning Surrogate Model-Based Optimization Approach] **

**Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or DOI https://doi.org/10.1080/xxxyyyzzzz]

## Description

This repository provides access to additional resources that enhance or support the findings presented in the main publication. These materials may include,

* **Datasets: ** The raw or processed datasets used for the analysis in the publication.

* **Python Code: ** Scripts or software implementations used for data analysis.

***Surrogate Model: ** Models generated

## Contents

1-Folder Dataset & Models ----------------------This folder has the Dataset and Trained Models

1-Individial Feature importance Score-----------This folder has the Feature importance Score of variables data

2-Reduced LHS-----------------------------------This folder have the Reduced LHS feature importance Score

## How to Use

The files in this repository are organized. You can browse the contents and download individual files as needed.

To access the main publication, please refer to: the publication's DOI link https://doi.org/10.1080/xxxyyyzzzz].

## License

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/

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## Citation

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.

## Contact

For any questions or inquiries regarding this repository or the publication, please contact:

**Last Updated:** April 02, 2025