1 code implementation • 27 Jul 2018 • Ruijin Cang, Hope Yao, Yi Ren
We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative process that computational design tasks usually entail.
1 code implementation • 7 Dec 2017 • Ruijin Cang, Hechao Li, Hope Yao, Yang Jiao, Yi Ren
Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces.
Computational Physics Materials Science