DualFluidNet: an Attention-based Dual-pipeline Network for FLuid Simulation

• URL: http://arxiv.org/abs/2312.16867v2
• Date: Thu, 18 Apr 2024 10:14:31 GMT
• Title: DualFluidNet: an Attention-based Dual-pipeline Network for FLuid Simulation
• Authors: Yu Chen, Shuai Zheng, Menglong Jin, Yan Chang, Nianyi Wang,
• Abstract summary: We propose an innovative approach for 3D fluid simulations utilizing an Attention-based Dual-pipeline Network. We find a way to achieve a better balance between global fluid control and physical law constraints. We also propose a new dataset, Tank3D, to further explore the network's ability to handle more complicated scenes.
• Score: 4.694954114339147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fluid motion can be considered as a point cloud transformation when using the SPH method. Compared to traditional numerical analysis methods, using machine learning techniques to learn physics simulations can achieve near-accurate results, while significantly increasing efficiency. In this paper, we propose an innovative approach for 3D fluid simulations utilizing an Attention-based Dual-pipeline Network, which employs a dual-pipeline architecture, seamlessly integrated with an Attention-based Feature Fusion Module. Unlike previous methods, which often make difficult trade-offs between global fluid control and physical law constraints, we find a way to achieve a better balance between these two crucial aspects with a well-designed dual-pipeline approach. Additionally, we design a Type-aware Input Module to adaptively recognize particles of different types and perform feature fusion afterward, such that fluid-solid coupling issues can be better dealt with. Furthermore, we propose a new dataset, Tank3D, to further explore the network's ability to handle more complicated scenes. The experiments demonstrate that our approach not only attains a quantitative enhancement in various metrics, surpassing the state-of-the-art methods but also signifies a qualitative leap in neural network-based simulation by faithfully adhering to the physical laws. Code and video demonstrations are available at https://github.com/chenyu-xjtu/DualFluidNet.

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