Flexible simulation package for optical neural networks

Here, we use a conditional deep convolutional generative adversarial network (cDCGAN) to inverse design across multiple classes of metasurfaces. Reference: https://onlinelibrary.wiley.com/doi/10.1002/adom.202100548

Simulations of photonic quantum programmable gate arrays

Optimization and inverse design of photonic crystals using deep reinforcement learning

Gentle introduction and demo of the adjoint variable method for electromagnetic inverse design

Pure Julia implementation of the finite difference frequency domain (FDFD) method for electromagnetics

Modeling and designing Photonic Crystal Nanocavities via Deep Learning

Adjoint-based optimization and inverse design of photonic devices.

Rigorous Coupled-Wave Analysis (RCWA) for nanophotonics simulations

2D multi-source electromagnetic simulations in frequency domain, implementing the augmented partial factorization (APF) and other methods.

Arrayed Waveguide Grating (AWG) model and simulation in Matlab

Free and open-source code package designed to perform PyMEEP FDTD simulations applied to Plasmonics (UBA+CONICET) [Buenos Aires, Argentina]

Julia implementation of Mie theory for nanophotonics

3D multi-source electromagnetic simulations in frequency domain, implementing the augmented partial factorization (APF) and other methods.

An nanophotonics solver for inverse design of metamaterials

Calculating optical cross sections from an arbitrary scatterer using surface integral equation.

The code for the work presented in the research paper titled "***"

3D multi-source electromagnetic simulations in frequency domain, implementing the augmented partial factorization (APF) and other methods.

Computational Photonics in Python with the finite element method. Mirror of https://gitlab.com/gyptis/gyptis

Here, we use Deep SHAP (or SHAP) to explain the behavior of nanophotonic structures learned by a convolutional neural network (CNN). Reference: https://pubs.acs.org/doi/full/10.1021/acsphotonics.0c01067