Documentation

Welcome to the LBMNet Documentation. This section provides the resources needed to understand, implement, and explore the LBMNet framework. Whether you are a researcher, student, or developer, the documentation is designed to help you get started and make the most of LBMNet.

Learn the fundamentals of LBMNet, its philosophy, and the core concepts behind physics-inspired deep learning based on the Lattice Boltzmann Method.

Follow the installation guide to set up the required environment, dependencies, and software needed to run LBMNet models.

Discover the design principles of LBMNet and understand how Lattice Boltzmann dynamics are integrated into modern neural network architectures.

Step-by-step tutorials demonstrate how to train, evaluate, and apply LBMNet to different computer vision and scientific computing tasks.

Browse available LBMNet architectures, including models for image restoration, segmentation, object detection, classification, super-resolution, and other AI applications.

Access information about the datasets commonly used for training and evaluating LBMNet models, along with recommended preprocessing procedures.

Explore the complete API documentation, including modules, classes, functions, parameters, and usage examples for developers.

A collection of practical examples illustrates how to use LBMNet in real-world projects, from research experiments to production-ready applications.

Compare the performance of LBMNet models across different datasets and tasks using standard evaluation metrics.

Find answers to common questions regarding installation, training, inference, troubleshooting, and best practices.

We encourage contributions from the research and open-source communities. Whether you are reporting bugs, improving documentation, or developing new LBMNet modules, your contributions are welcome.

If you encounter any issues or have questions about LBMNet, please visit the Help & Support page or contact the development team.

The LBMNet documentation is continuously evolving. New tutorials, examples, APIs, research updates, and implementation guides will be added regularly as the framework grows and new architectures become available.