Scripts for the 2-port Transfer Matrix Method (TMM) written in Julia.
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Updated
Nov 29, 2023 - Julia
Scripts for the 2-port Transfer Matrix Method (TMM) written in Julia.
Electrodynamics simulator for calculating the fields and potentials generated by moving point charges and simulating oscillating dipoles with and without periodic mechanical motion.
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.
Transmission matrix method code in momentum space for multilayer photonic structures.
The code for the work presented in the research paper titled "Nanophotonic Structure Inverse Design for Switching Application Using Deep Learning"
A machine learning repository used in my Bachelor Thesis for developing models for nanophotonics
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
Calculating optical cross sections from an arbitrary scatterer using surface integral equation.
RPExpand: Software for Riesz projection expansion of resonance phenomena.
This public repository is intended to allow users of the Diogenes software suite to submit bugs encountered.
The code for the work presented in the research paper titled "***"
Computational Photonics in Python with the finite element method. Mirror of https://gitlab.com/gyptis/gyptis
Modeling and designing Photonic Crystal Nanocavities via Deep Learning
Adjoint-based optimization and inverse design of photonic devices.
Julia implementation of Mie theory for nanophotonics
Arrayed Waveguide Grating (AWG) model and simulation in Matlab
An nanophotonics solver for inverse design of metamaterials
Free and open-source code package designed to perform PyMEEP FDTD simulations applied to Plasmonics (UBA+CONICET) [Buenos Aires, Argentina]
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
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