Laboratory of Optics of Quantum Materials's Projects
Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices
Python-based electromagnetic simulator and mode solver for nanophotonics applications, using the Eigenmode Expansion (EME) method.
ElATools: A tool for analyzing anisotropic elastic properties of the 2D and 3D materials
Global optimization based on generative neural networks
Diffractive Deep Neural Network. Image classification of Fashion-MNIST dataset using Python 3.6 and Tensorflow.
Band diagram and Field of 2D photonic cristal
Python based continuous adjoint optimization wrapper for Lumerical
Multilayer perceptron has been implemented using PyTorch framework to compute various optical properties of a photonic crystal fiber (PCF).
COMSOL implementation of the mesoscopic boundary conditions for nanoscale electromagnetism
A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.
Optical Neural Networks on PyTorch. diffractive propagation, nonlinear-photonic-activation
Pytorch Unofficial implement of paper "All optical machine learning using diffractive deep neural networks" .
Undergraduate course on Wave Optics (PH 202) for the Engineering Physics Program at IIT Bombay taught by Prof. Anshuman Kumar.
Student project codes from PH 421 (Photonics) course taught in Fall 2022 by Prof. Anshuman Kumar (IIT Bombay)
This repo is part of the undergraduate course PH 421: Photonics conducted by Prof. Anshuman Kumar in Fall 2023 in the Physics Department at IIT Bombay.
Student project codes from PH 444 (Electromagnetic Theory) course taught in Spring 2023 by Prof. Anshuman Kumar (IIT Bombay)
Student project codes from PH 421 (Photonics) course taught in Fall 2020 by Prof. Anshuman Kumar (IIT Bombay)
FDTD simulation tool for acoustic wave propagation in phononic crystal
Computing the Bandgap of a 2D Photonic Crystal by COMSOL-MATLAB Scripting
A C++ code for calculating the Purcell factor (decay rates both radiative and non radiative) of a dipole placed above a planar surfaces using Greens function approach.
PyLlama enables to calculate the reflection and transmission spectra of an arbitrary multilayer stack whose layers are made of dispersive or non-dispersive, absorbing or non absorbing, isotropic or anisotropic materials. The documentation and a few tutorials are available here: https://pyllama.readthedocs.io/en/latest/
Efficient and accurate inversion of multiple scattering with deep learning