Deep learning based design of thermal metadevices
Résumé
Thermal metadevices obtained from transformation optics have recently attracted wide attention due to their
vast potential for thermal management. However, these devices require extreme material parameters that are
difficult to achieve in large-scale applications. Here, we design a thermal concentrator using a machine learn-
ing method and demonstrate the thermal concentration performance of the designed device. We first define
an architecture with a single isotropic material. Deep learning models based on artificial neural networks are
implemented to retrieve design geometry parameters ensuring that the required spatially varying anisotropy
is achieved. We implement the optimized architecture into a thermal concentrator, fabricate samples and ex-
perimentally demonstrate that the designed metamaterial can simultaneously concentrate the heat flux in its
core and minimize perturbations to the external thermal field. Our approach paves new avenues for the design
of thermal management devices and, more generally, enables feasible solutions for inverse heat manipulation
problems.
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