Tiled Characteristic Maps for Tracking Detailed Liquid Surfaces

Narita, F.; Ando, Ryoichi

doi:10.1111/cgf.14638

Cited by 3 publications

(1 citation statement)

References 55 publications

(84 reference statements)

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“…Graph convolutional networks (GCNs) or GNN are trained on highfidelity SPH data to simulate forces between particles without time-consuming force calculations (Lin et al, 2023). The trained neural networks directly predict particle dynamics, leading to significant computational efficiency gains (Narita and Ando, 2022).…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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GARM-LS: A Gradient-Augmented Reference-Map Method for Level-Set Fluid Simulation

Li,

Ni,

Zhu

et al. 2023

ACM Trans. Graph.

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An Induce-on-Boundary Magnetostatic Solver for Grid-Based Ferrofluids

Ni,

Wang,

Wang

et al. 2024

ACM Trans. Graph.

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This paper introduces a novel Induce-on-Boundary (IoB) solver designed to address the magnetostatic governing equations of ferrofluids. The IoB solver is based on a single-layer potential and utilizes only the surface point cloud of the object, offering a lightweight, fast, and accurate solution for calculating magnetic fields. Compared to existing methods, it eliminates the need for complex linear system solvers and maintains minimal computational complexities. Moreover, it can be seamlessly integrated into conventional fluid simulators without compromising boundary conditions. Through extensive theoretical analysis and experiments, we validate both the convergence and scalability of the IoB solver, achieving state-of-the-art performance. Additionally, a straightforward coupling approach is proposed and executed to showcase the solver's effectiveness when integrated into a grid-based fluid simulation pipeline, allowing for realistic simulations of representative ferrofluid instabilities.

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Tiled Characteristic Maps for Tracking Detailed Liquid Surfaces

Abstract: Figure 1: Fountain with lucy statues. 300 × 200 × 300 resolution, 12 × 8 × 12 tiles. 54.9 seconds per video frame on average. Wired box and the color on the right visualize tiles and the map distortion, respectively.

Cited by 3 publications

References 55 publications

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

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

GARM-LS: A Gradient-Augmented Reference-Map Method for Level-Set Fluid Simulation

An Induce-on-Boundary Magnetostatic Solver for Grid-Based Ferrofluids

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