DeepAdjoint: An All-in-One Photonic Inverse Design Framework Integrating Data-Driven Machine Learning with Optimization Algorithms

no code implementations • 28 Sep 2022 • Christopher Yeung, Benjamin Pham, Ryan Tsai, Katherine T. Fountaine, Aaswath P. Raman

In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices.

Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures

no code implementations • 8 Sep 2022 • Christopher Yeung, Benjamin Pham, Zihan Zhang, Katherine T. Fountaine, Aaswath P. Raman

From higher computational efficiency to enabling the discovery of novel and complex structures, deep learning has emerged as a powerful framework for the design and optimization of nanophotonic circuits and components.

Computational Efficiency reinforcement-learning +1

Global Inverse Design Across Multiple Photonic Structure Classes Using Generative Deep Learning

no code implementations • 31 Dec 2020 • Christopher Yeung, Ryan Tsai, Benjamin Pham, Brian King, Yusaku Kawagoe, David Ho, Julia Liang, Aaswath P. Raman

Understanding how nano- or micro-scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge.

Optics