Simulation Methods for Multiphysics Phenomena in Visual Computing

2026-10-07

AI Summary

This study addresses the fragmentation of multiphysics simulation methods in visual computing, where unified mathematical frameworks and cross-material coupling strategies remain lacking. To this end, the project systematically reviews physical modeling algorithms for diverse materials, including rigid bodies and fluids, while thoroughly analyzing their mathematical foundations and software implementations. Methodologically, it integrates numerical solutions to partial differential equations, multi-body dynamics coupling algorithms, and deep learning-enhanced techniques to construct a unified theoretical framework that bridges traditional computer graphics with cutting-edge artificial intelligence. Ultimately, this work establishes a comprehensive knowledge system for multiphysics simulation encompassing multiple materials and complex interaction scenarios. It thereby provides a panoramic, standardized reference spanning from theoretical derivation to engineering practice for applications in gaming, virtual reality, and visual effects production.

Abstract

Physics simulation is a cornerstone of many computer graphics applications, ranging from video games and virtual reality to visual effects and computational design. The number of techniques for physically-based modeling and animation has thus skyrocketed over the past few decades, facilitating the simulation of a wide variety of materials and physical phenomena. These course notes provide an in-depth introduction to multiphysics simulation methods for computer graphics, covering the mathematical foundations, key algorithms, and practical considerations behind the most widely used approaches. We focus on methods developed by the computer graphics community for simulating various physical phenomena and materials -- including rigid and deformable bodies, fluids, and granular materials -- as well as the interactions between them. For each method, we present the underlying mathematical framework with detailed derivations and discuss how different materials and coupling strategies fit into the formulation. A selection of software frameworks that offer out-of-the-box multiphysics modeling capabilities is also presented. Finally, we touch on emerging trends in physics-based animation, including machine learning-based methods which have become increasingly popular in recent years.

Problem

• multiphysics simulation
• visual computing
• computer graphics
• physics-based animation
• material interaction

Innovation

• Multiphysics Simulation
• Visual Computing
• Coupling Strategies
• Physics-based Animation
• Machine Learning