Comparative Analysis of Neural Network-Based Methods for Wave Propagation Modeling

This repository contains a set of notebooks that demonstrate different deep learning techniques for simulating wave propagation, as described by the wave equation. The included notebooks are as follows:

1. Simple Function Approximation with PyTorch
2. Approximation of the 1D Wave Equation With Neural Networks Using PyTorch
3. Approximation of the 1D Wave Equation with Physics-Informed Neural Networks (PINNs) Using PyTorch
4. NNs-Based 1D Acoustic Wave Simulation with a Source Term
5. PINNs-Based 1D Acoustic Wave Simulation with a Source Term

Installation

We recommend setting up a new Python environment. You can do this by running the following commands:

conda create --name comparative-nn-wave-env
conda activate comparative-nn-wave-env

Next, clone this repository by using the command:

git clone https://github.com/oscar-rincon/NeuralNetworks-WavePropagation.git

Finally, go to the NeuralNetworks-WavePropagation/ folder and run the following command to install the necessary dependencies:

conda env update --name comparative-nn-wave-env --file comparative_nn_wave_env.yaml

To verify the packages installed in your comparative-nn-wave-env conda environment, you can use the following command:

conda list -n comparative-nn-wave-env