Theory and Applications of Gradient Free Optimization in Physics

Abstract

Machine Learning (ML) has become a popular field of research in many domains. It has become a flexible option to tackle a large variety of problems. This Thesis examines a fundamental component of ML training to explore how these tools can be further used in physics. The gained knowledge is then used for a physics inspired inverse design problem. This is done in three separate projects, the first explores gradient and non-gradient based learning, the second introduces adaptivity, and the final uses these concepts to learn how to grow photonic chips. My contributions for these projects includes the implementation, producing results and plot creations.