Deep learning-based inverse design of microstructured materials for optical optimization and thermal radiation control.

ABSTRACT: Microstructures with engineered properties are critical to thermal management in aerospace and space applications. Due to the overwhelming number of microstructure design variables, traditional approaches to material optimization can have time-consuming processes and limited use cases. Here, we combine a surrogate optical neural network with an inverse neural network and dynamic post-processing to form an aggregated neural network inverse design process. Our surrogate network emulates finite-difference time-domain simulations (FDTD) by developing a relationship between the microstructure's geometry, wavelength, discrete material properties, and the output optical properties. The surrogate optical solver works in tandem with an inverse neural network to predict a microstructure's design pro

SUBMITTER: Sullivan J 

PROVIDER: S-EPMC10164128 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature