Adjoint-based optimization and inverse design of photonic devices.

Flexible simulation package for optical neural networks

Simulations of photonic quantum programmable gate arrays

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

Julia implementation of Mie theory for nanophotonics

Arrayed Waveguide Grating (AWG) model and simulation in Matlab

RPExpand: Software for Riesz projection expansion of resonance phenomena.

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

A machine learning repository used in my Bachelor Thesis for developing models for nanophotonics

Modeling and designing Photonic Crystal Nanocavities via Deep Learning

This program is used to calculate the multipole decomposition of electric and magnetic fields in solid dielectric objects and to calculate the contribution of multipole resonances.

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

Calculate scattering cross section using Mie theory

Computes the optical properties (transmission, absorption, reflexion) of a multilayer system (dielectric or metallic layers), and the resulting 3D temperature distribution due to absorption. https://aip.scitation.org/doi/10.1063/5.0057185

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

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

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

This public repository is intended to allow users of the Diogenes software suite to submit bugs encountered.

Optimization and inverse design of photonic crystals using deep reinforcement learning

An nanophotonics solver for inverse design of metamaterials