Flexible simulation package for optical neural networks

Simulations of photonic quantum programmable gate arrays

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

Optimization and inverse design of photonic crystals using deep reinforcement learning

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

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

Rigorous Coupled-Wave Analysis (RCWA) for nanophotonics simulations

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

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

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

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

An nanophotonics solver for inverse design of metamaterials

Arrayed Waveguide Grating (AWG) model and simulation in Matlab

Adjoint-based optimization and inverse design of photonic devices.

Julia implementation of Mie theory for nanophotonics

Modeling and designing Photonic Crystal Nanocavities via Deep Learning

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

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

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

RPExpand: Software for Riesz projection expansion of resonance phenomena.