Thermal transparency with periodic particle distribution: A machine learning approach
Bin Liu, Liujun Xu, Jiping Huang
Abstract
The periodic interparticle interaction mechanism has been previously proposed for heat management, especially the practical application of thermal transparency. In our mechanism for engineering and manipulating thermal metamaterials, particles are arranged in periodic lattices with symmetric interactions. In this work, we relax the constraints in the previous work and allow rectangle lattice and arbitrary relative positioning between the two types of particles. We use a machine learning-based approach to solve the inverse design problem by training autoencoders to compress the dimensionalities of both the design space and the response space and training a neural network tailored for the inverse design problem. We employ the finite-element method for generating the training set for the neural network and for validating the calculated design parameters for a given thermal transparency problem. We also discuss the possibility of extending the machine learning-based workflow to other problems, such as thermal camouflage.