fdtd algorithm implementation using numpy and matplotlib python

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github.io tool

Finite Difference Time Domain (FDTD) Method with NumPy and Matplotlib

Implementation of the FDTD algorithm for simulating electromagnetic waves using Python with NumPy for numerical computations and Matplotlib for visualization.

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researchgate.net research

Numerical Solution of Maxwell's Equations using FDTD

This article presents a numerical solution of Maxwell's equations using the Finite Difference Time Domain (FDTD) method, implemented in Python using NumPy and Matplotlib for visualization.

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numpy.org article

FDTD Simulation using Python

Example code for implementing the FDTD algorithm using NumPy for numerical computations and Matplotlib for visualization, demonstrating the simulation of electromagnetic waves.

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ieee.org article

Python Implementation of FDTD for Electromagnetic Simulations

This paper discusses the implementation of the FDTD algorithm in Python for electromagnetic simulations, utilizing NumPy for efficient numerical computations and Matplotlib for visualization of results.

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youtube.com video

FDTD Algorithm Implementation in Python

Video tutorial demonstrating the implementation of the FDTD algorithm in Python using NumPy and Matplotlib, covering the basics of the FDTD method and its application in electromagnetic simulations.

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wikipedia.org article

Finite Difference Time Domain (FDTD) Method

The Finite Difference Time Domain (FDTD) method is a numerical analysis technique used for modeling computational electrodynamics. Python implementations using NumPy and Matplotlib are common for such simulations.

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pypi.org tool

Python FDTD Simulator

A Python package for simulating electromagnetic waves using the FDTD method, built on top of NumPy for numerical computations and Matplotlib for visualization, available for installation via PyPI.

C

coursera.org official

FDTD Simulations with Python and Matplotlib

Course materials including lectures and assignments on implementing the FDTD algorithm using Python with NumPy for numerical computations and Matplotlib for visualization, as part of a computational electromagnetics course.