Research
At LBMNet, our research focuses on developing the next generation of physics-inspired artificial intelligence architectures. We combine the mathematical principles of the Lattice Boltzmann Method (LBM) with modern deep learning techniques to create efficient, accurate, and interpretable neural networks for scientific computing and real-world AI applications. Our objective is to bridge computational physics and artificial intelligence by introducing architectures that learn through physical principles rather than relying solely on conventional convolutional operations
Research Areas
- Physics-Inspired Deep Learning
- Lattice Boltzmann Neural Networks (LBMNet)
- Computer Vision
- Medical Image Analysis
- Image Restoration and Inpainting
- Image Segmentation
- Image Super-Resolution
- Image Colorization
- Object Detection
- Face Recognition
- Pattern Recognition
- Scientific Machine Learning (SciML)
- Partial Differential Equations (PDEs)
- Fluid Dynamics-Inspired AI
- Medical Imaging
- AI for Healthcare
- Explainable and Efficient AI
Our Vision
We believe that future AI systems should not only achieve high performance but also incorporate scientific knowledge into their design. LBMNet aims to establish a new family of neural architectures inspired by the dynamics of the Lattice Boltzmann Method, offering improved efficiency, robustness, and physical interpretability for a wide range of computer vision and scientific computing tasks.
Current Research
Our ongoing research explores novel LBMNet architectures for:
- Image Inpainting
- Medical Image Segmentation
- MRI Denoising
- Super-Resolution
- Object Detection
- Image Classification
- Face Recognition
- Image Colorization
- Scientific Simulation
- PDE Learning and Solvers
Several research projects are currently under development and will be published in international journals and conferences as they become available.
Open Research
LBMNet is an active research initiative. We welcome collaborations with universities, research laboratories, industry partners, and students interested in physics-informed AI, computer vision, medical imaging, and scientific machine learning. Together, we aim to advance the future of intelligent systems through innovative, mathematically grounded neural architectures.