akapet00 / schrodinger

Comparison of numerical solutions of the 1-D time-independent Schrödinger equation obtained through FDM, FEM and the neural network approach.

schrodinger

schrodinger.ipynb notebook serves as the official seminar for the graduate course in Modern Physics and Technology FEMT08, taught by professor Ivica Puljak.

The paper titled Numerical Solution of the Schrödinger Equation Using a Neural Network Approach is based on this solver and is available at: https://ieeexplore.ieee.org/document/9238221.

Cite

@inProceedings{Kapetanovic2020,
    author={A. L. {Kapetanović} and D. {Poljak}},
    booktitle={2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)},
    title={Numerical Solution of the Schrödinger Equation Using a Neural Network Approach},
    year={2020},
    pages={1-5},
    doi={10.23919/SoftCOM50211.2020.9238221}}

Installation

Clone the repo onto your local machine:

$ git clone https://github.com/antelk/schrodinger.git

Access schrodinger directory:

$ cd schrodinger

Create conda environment to avoid compatibility issues:

$ conda env create -n schrodinger -f environment.yml

Use

Activate the environment:

$ conda activate schrodinger

Run:

$ jupyter notebook

Remove from your local machine

Remove the environment and its dependencies

$ conda remove -n schrodinger --all

License

MIT