Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction

• URL: http://arxiv.org/abs/2406.03789v2
• Date: Thu, 17 Oct 2024 00:44:07 GMT
• Title: Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
• Authors: Sunwoong Yang, Ricardo Vinuesa, Namwoo Kang,
• Abstract summary: We explore the potential of Graph U-Nets for unsteady flow-field prediction. We propose novel approaches to improve mesh-agnostic-temporal robustness prediction transient flow fields using Graph U-Nets. Key enhancements to the Graph U-Net architecture provide increased flexibility in modeling node dynamics.
• Score: 2.3964255330849356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study aims to overcome the limitations of conventional deep-learning approaches based on convolutional neural networks in complex geometries and unstructured meshes by exploring the potential of Graph U-Nets for unsteady flow-field prediction. We present a comprehensive investigation of Graph U-Nets, originally developed for classification tasks, now tailored for mesh-agnostic spatio-temporal forecasting of fluid dynamics. Our focus is on enhancing their performance through systematic hyperparameter tuning and architectural modifications. We propose novel approaches to improve mesh-agnostic spatio-temporal prediction of transient flow fields using Graph U-Nets, enabling accurate prediction on diverse mesh configurations. Key enhancements to the Graph U-Net architecture, including the Gaussian-mixture-model convolutional operator and noise injection approaches, provide increased flexibility in modeling node dynamics: the former reduces prediction error by 95\% compared to conventional convolutional operators, while the latter improves long-term prediction robustness, resulting in an error reduction of 86\%. We demonstrate the effectiveness of these enhancements in both transductive and inductive learning settings, showcasing the adaptability of Graph U-Nets to various flow conditions and mesh structures. This work contributes to the field of reduced-order modeling for computational fluid dynamics by establishing Graph U-Nets as a viable and flexible alternative to convolutional neural networks, capable of accurately and efficiently predicting complex fluid flow phenomena across diverse scenarios.

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