This repository contains a collection of projects that leverage neural networks in PyTorch to explore various topics including chess, physics, and differential equations.
The repository is organized into different folders, each representing a separate project. Here is an overview of the projects included:
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chess-ai: This project focuses on developing a chess-playing AI using neural networks. The AI will be trained to evaluate chess positions and make intelligent moves. -
physics-simulation: This project involves using neural networks to simulate physical systems and solve partial differential equations (PDEs). The goal is to train a model to learn the dynamics of a physical system based on its initial conditions and equations. -
differential-equation-solvers: In this project, we build neural networks capable of solving ordinary and partial differential equations. The models will be trained to approximate the solutions to various types of differential equations. -
chaos-dynamical-systems: This project explores the behavior of chaotic systems and dynamical systems using neural networks. The models will be trained to predict the long-term evolution of chaotic systems or analyze the bifurcation points in dynamical systems. -
chess-move-prediction: The focus of this project is to train a neural network to predict chess moves given a board position. Convolutional neural networks (CNNs) will be used to analyze the board state and make move predictions.
Each project may have specific requirements and dependencies. Please refer to the README.md file inside each project folder for detailed instructions on setting up the environment and running the code.
To get started with a specific project, navigate to the corresponding project folder and follow the instructions provided in the README.md file.
Contributions to the projects are welcome! If you have any ideas, improvements, or bug fixes, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License.