Purpose

The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.

Design/methodology/approach

This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.

Findings

FEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.

Originality/value

The originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).

中文翻译：

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通过具有自适应滤波器和聚合器融合的图神经网络对液体飞溅进行高效建模

 目的

本研究的目的是推进液体飞溅动力学的计算建模，同时平衡模拟精度和计算效率，这是高保真流体动力学模拟中经常受到损害的二元性。

设计/方法论/途径

本研究介绍了流体高效图神经网络模拟器（FEGNS），这是一种创新框架，在图神经网络架构中集成了自适应过滤层和聚合器融合策略。 FEGNS 旨在直接从大量液体飞溅数据集中学习，捕获复杂的动力学和本质上复杂的相互作用。

 发现

与传统方法相比，FEGNS 的模拟精度显着提高了 30.3%，计算速度提高了 51.6%。它在不同的材料中表现出强大的泛化能力，能够对液滴效应进行真实的模拟。比较分析和实证验证表明，FEGNS 相对于现有基准模型具有优越的性能。

 原创性/价值

FEGNS的独创性在于它的自适应过滤层，它独立地调整每个节点的过滤权重，以及一种新颖的聚合器融合策略，通过组合多个聚合函数来丰富网络的表达能力。为了便于进一步研究和实际部署，FEGNS 模型已在 GitHub 上开放（https://github.com/nanjinyao/FEGNS/tree/main）。