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Book Open access Jul 2026

Fluid Simulation with the Lattice Boltzmann Method

Over the past decade, the Lattice Boltzmann Method (LBM) has matured into a leading framework for high-performance fluid simulation across research and industry alike. Its appeal rests on a set of properties that align unusually well with the demands of modern computing: collision and streaming operators that are strictly local, mapping naturally onto massively parallel architectures; an intrinsic ability to handle complex geometries through simple boundary rules; and a level of physical fidelity that has proven sufficient across a widening range of applications — from real-time simulation and visual effects to large-scale industrial tools such as virtual wind tunnels. The growing body of LBM-based work at SIGGRAPH and SIGGRAPH Asia reflects this trajectory directly: the method has moved from occasional appearance to recurring presence as a core computational framework. Despite this prominence, LBM has never been the subject of a dedicated SIGGRAPH course. To date, it has only been mentioned peripherally, most notably as a small component of the Real-Time Physics course at SIGGRAPH 2004. As a result, there remains a significant gap between the method’s practical importance and its formal coverage within the SIGGRAPH educational program. This lack of structured exposure has made it difficult for practitioners and researchers to fully understand the method’s foundations, strengths, limitations, and best practices for deployment on contemporary hardware. These course notes are an attempt to close that gap. Written by authors with direct experience across multiple LBM publications at SIGGRAPH over the past six years, they aim to provide something the existing literature rarely offers in a single place: a coherent path from first principles to production-oriented practice. The intent is not to survey recent work, but to equip attendees with the concepts, implementation details, and the critical perspective needed to start working with LBM solvers.

Wei Li, Chaoyang Lyu, Mengyun Liu et al. · 0 citations

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