• Ravi Bhushan

This study explores the application of machine learning (ML) techniques to optimize optical designs and predict optical behaviours. Traditional methods in optics design often rely on iterative processes and complex simulations, which can be time-consuming and computationally intensive. By contrast, ML offers a promising alternative by leveraging large datasets and powerful algorithms to accelerate design optimization and enhance predictive capabilities. Key areas of focus include data preprocessing, feature selection, model training, and validation. The study evaluates various ML algorithms such as neural networks, support vector machines, and decision trees, comparing their effectiveness in predicting optical performance metrics. Results demonstrate significant improvements in design efficiency and accuracy, highlighting ML's potential to revolutionize optical engineering practices. Future research directions include expanding datasets, refining algorithms, and integrating ML with other optimization techniques for further advancements in optical design.

Machine Learning, Optical Design, Predictive Modelling, Algorithm Optimization, Neural Networks, Support Vector Machines

"A Study for implementation of machine learning techniques for optimizing optical designs and predicting optical behaviours", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.h517-h524, December-2023, Available :http://www.jetir.org/papers/JETIR2312765.pdf

"A Study for implementation of machine learning techniques for optimizing optical designs and predicting optical behaviours", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. pph517-h524, December-2023, Available at : http://www.jetir.org/papers/JETIR2312765.pdf