Research on the Application of Physical Information Neural Network in Multiscale Fluid Dynamics Simulation

Jinyu Liu; Yinxiao Yan; Junhui Wang

doi:10.62051/ijcsit.v5n3.11

Research on the Application of Physical Information Neural Network in Multiscale Fluid Dynamics Simulation

Jinyu Liu, Yinxiao Yan, Junhui Wang

International Journal of Computer Science and Information Technology, (2025), Vol.5, No.3, pp.109-116

Published: April 10, 2025

DOI: 10.62051/ijcsit.v5n3.11

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Abstract

In this paper, a multi-scale fluid dynamics simulation framework based on physical information neural network (PINN) is proposed, which achieves efficient simulation of multi-scale flow characteristics by means of a multi-resolution network structure, physical constraints on scale decomposition and adaptive sampling strategy. Experimental results show that the framework outperforms traditional methods in microscale flow, turbulence and multiphase flow problems, with a 37% improvement in the average prediction accuracy and a 75% reduction in the computational resource requirement. In particular, it shows excellent generalisation performance when dealing with complex flows with large scale spans, providing a new method for fluid dynamics simulation with both accuracy and efficiency.

Keywords: Physically informed neural networks, Multiscale fluid simulation, Deep learning, Turbulence simulation, Microscale flow, Multiphase flow, Adaptive sampling

APA Citation: Jinyu Liu, Yinxiao Yan, Junhui Wang (2025). Research on the Application of Physical Information Neural Network in Multiscale Fluid Dynamics Simulation. International Journal of Computer Science and Information Technology, 5(3), 109-116. https://doi.org/10.62051/ijcsit.v5n3.11

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