Particle trajectory reconstruction method and device based on flow state perception implicit neural representation, equipment and medium

By employing a particle trajectory reconstruction method based on flow-perception implicit neural representation, and combining multilayer perceptron and implicit neural representation models with Taylor expansion and automatic differentiation techniques, the particle trajectory reconstruction model is optimized. This solves the problems of high computational resource consumption and limited accuracy of traditional methods in complex flow fields, and achieves efficient and high-precision particle trajectory reconstruction.

CN122088216BActive Publication Date: 2026-07-03CALCULATION AERODYNAMICS INST CHINA AERODYNAMICS RES & DEV CENT
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-23
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Traditional particle trajectory reconstruction methods consume large amounts of computational resources and have limited accuracy in complex flow fields, especially when dealing with high-dimensional data and complex flow fields.

Method used

The particle trajectory reconstruction method based on fluid-sensing implicit neural representation constructs a spatiotemporal position embedding network using a multilayer perceptron and implicit neural representation model. It combines Taylor expansion and automatic differentiation techniques and optimizes the particle trajectory reconstruction model through a multi-scale residual learning module and target constraints.

Benefits of technology

It achieves high-precision particle trajectory reconstruction, effectively restores the particle motion law of complex flow fields, and improves reconstruction efficiency and accuracy.

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Abstract

This application discloses a particle trajectory reconstruction method, apparatus, device, and medium based on flow-sensing implicit neural representation, relating to the field of computer technology. The method includes: constructing a spatiotemporal position embedding network based on Eulerian flow field information of the flow field surrounding an aircraft, utilizing a multilayer perceptron and implicit neural representation model to capture the spatiotemporal distribution and motion patterns of particles in the flow field around the aircraft; constructing a multi-scale residual learning module using Taylor expansion to balance trajectory learning at different time scales; constructing an initial particle trajectory reconstruction model using the spatiotemporal position embedding network and the multi-scale residual learning module; training the initial particle trajectory reconstruction model using target constraints; generating a target motion trajectory using the obtained target particle trajectory reconstruction model and based on the position information and integration time of the target particle in the flow field; where the integration time is the length of the target particle's motion time in the flow field. This improves the efficiency of particle trajectory reconstruction in complex flow fields.
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