Special issue: Elastic computing from edge to the cloud environments

Ilager, Shashikant; Stankovski, Vlado; Pallickarar, Shrideep; Buyya, Rajkumar

doi:10.1002/spe.3012

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Our proposed FEGNS incorporates adaptive filtering and fusion techniques into a tailored GNN architecture, enhancing accuracy and efficiency. Inspired by Zhou et al ’s (2022) work integrating the Gustafson–Kessel clustering algorithm with an adaptive recurrent fuzzy neural network, FEGNS achieves remarkable performance, attaining 92% accuracy on simulations involving over 10,000 liquid particles, while realizing a 60% reduction in computational time compared to conventional techniques (Skoulikaris and Piliouras, 2023; Hewage et al , 2024). This achievement marks a advancement toward efficient fluid simulation, pioneering the integration of advanced adaptive filtering and aggregator fusion techniques into the field of liquid splashing modeling (Hwang and Ishii, 2024; Burgard et al , 2023).…”

Section: Related Methodsmentioning

confidence: 99%

Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion

Nan,

Feng,

et al. 2024

HFF

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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).

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