curses 简介cureses 最早是由柏克莱大学的 Bill Joy 及 Ken Arnold所发展出来的.当时发展此函数库的主要原因是为了提高程序对不同终端机的兼容性而设计的.因此,利用 curses 发展出来的程序将和您所使用的终端机无关.也就是说,您不必...curses 简介cureses 最早是由柏克莱大学的 Bill Joy 及 Ken Arnold所发展出来的.当时发展此函数库的主要原因是为了提高程序对不同终端机的兼容性而设计的.因此,利用 curses 发展出来的程序将和您所使用的终端机无关.也就是说,您不必担心您的程序因为换了一部终端机而无法使用.这对程序设计师而言,尤其是网路上程序的编写,是件相当重要的一件事.展开更多
Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challe...Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.展开更多
文摘curses 简介cureses 最早是由柏克莱大学的 Bill Joy 及 Ken Arnold所发展出来的.当时发展此函数库的主要原因是为了提高程序对不同终端机的兼容性而设计的.因此,利用 curses 发展出来的程序将和您所使用的终端机无关.也就是说,您不必担心您的程序因为换了一部终端机而无法使用.这对程序设计师而言,尤其是网路上程序的编写,是件相当重要的一件事.
基金supported by the National Key Research and Development Projects (Grant Nos.2021YFB3300601,2021YFB3300603,2021YFB3300604)Fundamental Research Funds for the Central Universities (No.DUT22QN241).
文摘Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.