About the Founder

Ph.D. Candidate in Computer Science

LBMNet was founded with a single ambition: to redefine artificial intelligence through the principles of physics.

I am Yassine Douich, a Ph.D. candidate in Computer Science at the Faculty of Sciences, Chouaib Doukkali University, Morocco. My research is dedicated to Artificial Intelligence, Scientific Machine Learning, Computer Vision, and the Lattice Boltzmann Method, with a particular focus on designing next-generation neural architectures inspired by physical systems.

IThroughout my research, I have explored a fundamental question:

Can the mathematical principles governing physical phenomena inspire a new generation of artificial intelligence?

This question led to the creation of LBMNet, a research initiative that integrates the computational principles of the Lattice Boltzmann Method into deep learning. Rather than relying solely on conventional neural network designs, LBMNet investigates architectures where information propagates through physics-inspired interactions, offering new perspectives on efficiency, interpretability, and scientific modeling.

Today, LBMNet serves as an open research platform dedicated to advancing Physics-Inspired Artificial Intelligence. Its mission is to develop innovative architectures capable of addressing challenging problems across scientific computing, computer vision, medical imaging, time-series analysis, biomedical engineering, and computational science.

Beyond research, I have more than eighteen years of experience teaching mathematics. This background has shaped my scientific approach, emphasizing rigorous mathematical foundations, numerical analysis, and the integration of physical laws into modern AI systems.

My research spans several domains, including computer vision, medical image analysis, image restoration, image segmentation, time-series forecasting, scientific computing, cybersecurity, biomedical signal analysis, and scientific machine learning. Through LBMNet, I aim to create innovative architectures capable of solving complex real-world problems while remaining grounded in scientific principles.

To establish LBMNet as a global reference for physics-inspired artificial intelligence by developing innovative deep learning architectures grounded in the mathematical principles of the Lattice Boltzmann Method.

• Advance the frontiers of Physics-Inspired Artificial Intelligence.
• Develop novel Lattice Boltzmann-based neural architectures.
• Bridge numerical physics and modern deep learning.
• Promote open scientific research and reproducible AI.
• Support researchers, students, and engineers through open-source software, publications, and educational resources.
• Foster international collaborations in Artificial Intelligence and Scientific Computing.

• Physics-Inspired Artificial Intelligence
• Scientific Machine Learning
• Lattice Boltzmann Deep Learning
• Computer Vision
• Medical Image Analysis
• Image Restoration and Inpainting
• Image Segmentation
• Biomedical Signal Processing
• Time-Series Forecasting
• Scientific Computing
• Numerical Methods for Partial Differential Equations

LBMNet represents more than a research project. It reflects a long-term commitment to advancing scientific innovation by combining mathematics, physics, and artificial intelligence. The goal is not only to develop high-performance AI models but also to contribute to a deeper scientific understanding of intelligent systems inspired by the laws of nature.

Thank you for visiting LBMNet. I invite you to explore our research, discover our architectures, and join us in shaping the future of physics-inspired artificial intelligence.