Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer bas...Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.展开更多
文摘Multi-target digital material design has been challenging due to the expansive design space and instability of traditional methods in satisfying multiple objectives.This work proposes and demonstrates a customizer based on a classifier-free,conditional denoising diffusion probability model(cDDPM)to efficiently create the layouts of digital materials meeting the design goal of multiple mechanical properties all together.A case study has been conducted based on a micro mechanical resonator with four pre-assigned resonant frequencies.Using 29,430 samples generated via finite element analysis(FEA),the cDDPM is trained to simultaneously customize up to four vibrational modes,achieving over 95%prediction accuracy.Furthermore,the cDDPM approach also shows superior performances in the single-target customization for up to 99%in prediction accuracy when compared with traditional conditional generative adversarial networks(cGANs).As such,the proposed design framework provides a highly customizable and robust methodology for the design of complicated digital materials.
