Additional Reference
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Additional references that might be helpful to understand the hyperparameter optimization and the coding provided in this documentation.

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Documentations 
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Here are the URLs for the documentation of the libraries and algorithms used in this research, covering mathematical tools, plotting tools, and optimization algorithms:

- **Numpy** : https://numpy.org/

- **Pandas** : https://pandas.pydata.org/

- **Hpbandster**: https://automl.github.io/HpBandSter/build/html/quickstart.html 

- **Ax** : https://ax.dev/ 

- **Matplotlib** : https://matplotlib.org/ 

- **Scipy** : https://scipy.org/ 

- **Sklearn** : https://scikit-learn.org/stable/ 

- **Tensorflow** : https://www.tensorflow.org/ 

	
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Papers 
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There are three papers and one Master’s thesis published on hyperparameter optimization (HPO), listed chronologically from oldest to newest. 

- **First Paper** :

Jeong, T., Koratikere, P., & Leifsson, L. T. (2024). Automated Hyperparameter Tuning for Airfoil Shape Optimization with Neural Network Models. In AIAA SCITECH 2024 Forum (p. 2671). https://doi.org/10.2514/6.2024-2671

- **Second Paper** :

Jeong, T., Koratikere, P., Leifsson, L., Koziel, S., & Pietrenko-Dabrowska, A. (2024, June). Adaptive Hyperparameter Tuning Within Neural Network-Based Efficient Global Optimization. In International Conference on Computational Science (pp. 74-89). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63775-9_6

- **Master's Thesis** :

`Taeho Jeong's Thesis <https://hammer.purdue.edu/authors/Taeho_Jeong/18437064>`_. 

- **Third Paper** (Not yet published):

Jeong, T., Koratikere, P., & Leifsson, L. T. (2024). Adaptive Hyperparameter Optimization Strategies for Neural Network Models in Engineering Design Optimization