🧠 Notebooks_NN_Physics

📌 Description This repository contains Jupyter Notebooks developed as supplementary material for the article "Introduction to Neural Networks for Physicists", published in the Arxiv. Created by Gustavo Café and Gubio Gomes, these notebooks aim to enhance the understanding of the concepts discussed in the article by providing practical and interactive examples. Specifically, they explore different neural network approaches applied to the dynamics of a simple pendulum, illustrating their potential in computational physics.

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📂 Repository Structure

📁 Notebooks

📌 File	📖 Description
🟢 01-Perceptron-iris.ipynb	Introduction to the Perceptron algorithm applied to the Iris dataset.
🟠 02-Perceptron-Regressao.ipynb	Using the Perceptron for regression problems.
🔵 03-Perceptron-iris.ipynb	Another implementation of the Perceptron on the Iris dataset with specific adjustments.
⚙️ 04-Exemplo 1 Pendulo.ipynb	Determine the constant for OHS (Simple Harmonic Oscillation) when modeling a pendulum using neural networks.
🔥 05-Exemplo 2 OHS.ipynb	Solve diferential equation of OHS with method call Physics informed neural network.
🏗️ ./Exemplo 3-Autoencoder	Implementation of an Autoencoder for compression and representation learning.
🤖 ./SINDyAutoencoder	Use of Sparse Identification of Nonlinear Dynamics (SINDy) with Autoencoders.

📊 Other Files

• 🖼️ SINDyAutoencoder_ValidationData_PT.png - Validation figure for the SINDyAutoencoder model.

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🚀 How to Use

1️⃣ Clone the repository

 git clone https://github.com/Coffee4MePlz/Notebooks_NN_Physics.git

2️⃣ Install dependencies (if needed)

 pip install -r requirements.txt

3️⃣ Run the notebooks

• Open Jupyter Notebook:
• Navigate to the desired notebook and execute the cells.

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🔧 Requirements

• Python 3.x
• Jupyter Notebook
• Libraries: NumPy, TensorFlow, SciPy, Matplotlib (details in requirements.txt)

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📬 Contact

If you have any questions or suggestions, feel free to reach out: 📧 Email: gcaf0125@uni.sydney.edu.au
🔗 LinkedIn: My Profile

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