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Heat management is crucial for state-of-the-art applications such as passiveradiative cooling, thermally adjustable wearables, and camouflage systems.Their adaptive versions, to cater to varied requirements, lean on the potentialof adaptive metamaterials. Existing efforts, however, feature with highlyanisotropic parameters, narrow working-temperature ranges, and the need formanual intervention, which remain long-term and tricky obstacles for the mostadvanced self-adaptive metamaterials. To surmount these barriers, we introduceheat-enhanced thermal diffusion metamaterials powered by deep learning. Suchactive metamaterials can automatically sense ambient temperatures and swiftly,as well as continuously, adjust their thermal functions with a high degree oftunability. They maintain robust thermal performance even when external thermalfields change direction, and both simulations and experiments demonstrateexceptional results. Furthermore, we design two metadevices with on-demandadaptability, performing distinctive features with isotropic materials, wideworking temperatures, and spontaneous response. This work offers a frameworkfor the design of intelligent thermal diffusion metamaterials and can beexpanded to other diffusion fields, adapting to increasingly complex anddynamic environments.

Deep learning-assisted active metamaterials with heat-enhanced thermal transport