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Finite element mesh optimization method and device based on deep reinforcement learning and medium

A technology of reinforcement learning and finite element, applied in design optimization/simulation, character and pattern recognition, biological neural network model, etc., can solve the problem that the grid cannot fully meet the analysis and calculation, and achieve the effect of improving quality and efficiency

Active Publication Date: 2021-08-06
中汽数据(天津)有限公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

At present, the BatchMesh function of mainstream commercial software can complete the preliminary grid division, but the grid generated by it cannot fully meet the requirements of analysis and calculation, and there is still a lot of work that needs manual operation to optimize and adjust

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  • Finite element mesh optimization method and device based on deep reinforcement learning and medium
  • Finite element mesh optimization method and device based on deep reinforcement learning and medium
  • Finite element mesh optimization method and device based on deep reinforcement learning and medium

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Embodiment Construction

[0036] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0037] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the refe...

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Abstract

The invention relates to the field of grid division, in particular to a finite element grid optimization method and device based on deep reinforcement learning and a medium, and the method comprises the steps: obtaining initial finite element grid data of a geometric model; generating a three-dimensional model of the geometric model according to the initial finite element grid data; performing feature recognition and classification on the three-dimensional model by adopting a detection network to obtain local features of the three-dimensional model and a classification result of the local features; inputting the unreasonable local features into an optimization network so as to enable the grid quality of the adjusted finite element grid data to meet a set requirement; capturing a readjustment strategy of a user on the adjusted finite element grid data, and performing reinforcement learning on the optimization network by taking the readjustment strategy as forward excitation so as to update the optimization network; and continuing to optimize the adjusted finite element grid data by adopting the updated optimization network. According to the embodiment of the invention, the accuracy of grid division can be improved.

Description

technical field [0001] The present invention relates to the field of grid division, in particular to a finite element grid optimization method, device and medium based on deep reinforcement learning. Background technique [0002] The common methods of finite element mesh division include geometry coded topology, graph coded topology, similarity heuristic topology and character coded topology. In mainstream computer-aided engineering (Computer Aided Engineering, CAE) simulation software, the bottom layer of mesh division is realized by geometric coding topology. [0003] CAE is a very important link in the process of automobile research and development, among which mesh division is the most basic and important pre-work. Grid work accounts for about 50% of the workload of automotive R&D simulation, and the quality of the grid also has a greater impact on the simulation results. Therefore, how to improve the efficiency of grid division and ensure the quality of the grid is a ...

Claims

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Application Information

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IPC IPC(8): G06F30/23G06K9/62G06N3/04
CPCG06F30/23G06N3/045G06F18/214Y02T10/40
Inventor 商庆亮孙宇
Owner 中汽数据(天津)有限公司
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