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Afsahi, Asef. (1405). Predictive Modeling of Fluid Dynamics using Graph Neural Networks: A Benchmark Evaluation on Exact Navier-Stokes Solutions. , 58(1), 1-12. doi: 10.22059/jac.2026.412728.1256
Asef Afsahi. "Predictive Modeling of Fluid Dynamics using Graph Neural Networks: A Benchmark Evaluation on Exact Navier-Stokes Solutions". , 58, 1, 1405, 1-12. doi: 10.22059/jac.2026.412728.1256
Afsahi, Asef. (1405). 'Predictive Modeling of Fluid Dynamics using Graph Neural Networks: A Benchmark Evaluation on Exact Navier-Stokes Solutions', , 58(1), pp. 1-12. doi: 10.22059/jac.2026.412728.1256
Afsahi, Asef. Predictive Modeling of Fluid Dynamics using Graph Neural Networks: A Benchmark Evaluation on Exact Navier-Stokes Solutions. , 1405; 58(1): 1-12. doi: 10.22059/jac.2026.412728.1256

Predictive Modeling of Fluid Dynamics using Graph Neural Networks: A Benchmark Evaluation on Exact Navier-Stokes Solutions

Journal of Algorithms and Computation
دوره 58، شماره 1، مهر 2026، صفحه 1-12    اصل مقاله (3.05 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2026.412728.1256
نویسنده
Asef Afsahi*
School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
چکیده
Traditional Computational Fluid Dynamics (CFD) methods demand significant computational resources. Graph Network-based Simulators (GNS) have emerged as powerful surrogates, modeling complex physical systems by learning spatial-temporal interactions. In this paper, we evaluate a GNS model on nine exact analytical solutions of the Navier-Stokes equations, comprising four 2D and five 3D flows. By training the network to predict fluid particle evolution using localized message passing, we bypass Eulerian grid constraints and simulate fluid topologies in a Lagrangian framework. Our results demonstrate that the GNS approach achieves highly accurate rollouts (MSE $< 0.01$) while successfully capturing complex structures like viscous vortex decay. The model achieves its lowest rollout Mean Total MSE of $3.7852 \times 10^{-3}$ on the Lamb-Oseen 2D model, highlighting the generalizability and precision of deep graph architectures for exact fluid mechanics phenomena.
کلیدواژه‌ها
Graph Neural Networks؛ Computational Fluid Dynamics؛ Navier-Stokes Equations؛ Reduced Order Modeling؛ Particle-Based Simulation
آمار
تعداد مشاهده مقاله: 108
تعداد دریافت فایل اصل مقاله: 27





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