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Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructured simulation meshes.
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[Online]. Available: https://github.com/NVIDIA/modulus
Modulus Contributors, “NVIDIA Modulus: An open-source framework for physics-based deep learning in science and engineering,” 2023 · 2023
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L. Pegolotti, M. R. Pfaller, N. L. Rubio, K. Ding, R. B. Brufau, E. Darve, and A. L. Marsden, “Learning reduced-order models for cardiovascular simulations with graph neural networks,” Computers in Biology and Medicine
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M. Elrefaie, F. Ahmed, and A. Dai, “Drivaernet: A parametric car dataset for data-driven aerodynamic design and graph-based drag prediction,” in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
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2022
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T. L. Trinh, F. Chen, T. Nanri, and K. Akasaka, “3d super-resolution model for vehicle flow field enrichment,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
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