Method for accelerating structure optimization design by applying generative adversarial network

A technology for network acceleration and optimization design, applied in biological neural network models, neural learning methods, computing, etc., can solve problems such as taking a long time, and achieve the effect of improving discrimination ability, reducing computational complexity, and fast computing

Active Publication Date: 2019-05-21
XI AN JIAOTONG UNIV
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In order to design an efficient material structure distribution under given load conditions, constraints and performance indicators, researchers at home and abroad use topology optimization methods to optimize the design; the basic idea of ​​​​topology optimization is to find the optimal topology of the structure Transformed into the distribution problem of seeking the optimal material in a given design area, the current topology optimization methods mainly include SIMP algorithm, ESO algorithm, level set method, etc. The calculation amount of these methods depends on the size of the grid. The continuous increase of the calculation amount increases exponentially, making it take a long time to obtain the optimal design results

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for accelerating structure optimization design by applying generative adversarial network
  • Method for accelerating structure optimization design by applying generative adversarial network
  • Method for accelerating structure optimization design by applying generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] Below in conjunction with accompanying drawing and embodiment the present invention is described in detail;

[0026] The present invention provides a structural optimization design method for application generation confrontation network acceleration; figure 1 As shown, a structural optimization design method using generative confrontation network acceleration, including the following steps: Step 1: Prepare data using SIMP algorithm; Step 2: Use data enhancement technology to expand the data set; Step 3: Use encoder-decoder Build generator; Step 4: use deep convolutional network to construct discriminator; Step 5: use deformed pix2pix model to train; Step 6: use final model; the present invention has the advantages of accurately generating optimized structure, greatly reducing computational complexity, Advantages of reducing computational overhead.

[0027] The steps to generate a structural optimization design method against network acceleration are as follows:

[002...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for accelerating structure optimization design by applying a generative adversarial network. The acceleration method can solve the problem that a traditional SIMP algorithm is high in calculation complexity in structural optimization design, the method comprises two parts of model obtaining and model using, and the main process of model obtaining comprises the steps that 1, a small number (100 sets) of optimization process diagrams are generated in advance through the SIMP method to serve as a training set and a test set; 2, expanding the data set by using a data enhancement technology; 3, using an encoder-decoder to construct a generator; 4, constructing a discriminator by using a deep convolutional network; 5, using the deformed pix2pix model to train a generator-discriminator and display a training result and stores a final training model. When the final model is used for structure optimization design, firstly, a small number of iteration steps are calculated through an SIMP method, an iteration result is input into the final model for calculation, and therefore rapid calculation of the final optimized structure is achieved.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a method for reducing computational complexity to the greatest extent on the premise of ensuring the correctness of the generated results by applying a structural optimization design technology for generating confrontational network acceleration. Background technique [0002] In order to design an efficient material structure distribution under given load conditions, constraints and performance indicators, researchers at home and abroad use topology optimization methods to optimize the design; the basic idea of ​​​​topology optimization is to find the optimal topology of the structure Transformed into the distribution problem of seeking the optimal material in a given design area, the current topology optimization methods mainly include SIMP algorithm, ESO algorithm, level set method, etc. The calculation amount of these methods depends on the size of the grid. ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/04G06N3/08
Inventor 郑帅何真真黄从甲田智强李宝童
Owner XI AN JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products