A generative flow for conditional sampling via optimal transport

1 code implementation • 9 Jul 2023 • Jason Alfonso, Ricardo Baptista, Anupam Bhakta, Noam Gal, Alfin Hou, Isa Lyubimova, Daniel Pocklington, Josef Sajonz, Giulio Trigila, Ryan Tsai

Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation.

Bayesian Inference Density Estimation

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.

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