Graph neural network-accelerated Lagrangian fluid simulation

• Published in: Computers & graphics Vol. 103; pp. 201 - 211
• Main Authors: Li, Zijie, Farimani, Amir Barati
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2022; Elsevier Science Ltd
• Subjects: Acceleration; Data-driven modeling; Fluid flow; Forecasting; Graph neural networks; Graphical representations; Incompressible flow; Incompressible fluids; Iterative methods; Neural networks; Particle-based fluid dynamics; Physical properties; Simulation; Data-driven modeling; Particle-based fluid dynamics; Graph neural networks

We present a data-driven model for fluid simulation under Lagrangian representation. Our model, Fluid Graph Networks (FGN), uses graphs to represent the fluid field. In FGN, fluid particles are represented as nodes and their interactions are represented as edges. Instead of directly predicting the a...