Design and validation of world-class multilayered thermal emitter using machine learning

NIMS, the University of Tokyo, Niigata University and RIKEN have jointly designed a multilayered metamaterial that realizes ultra-narrowband wavelength-selective thermal emission by combining the machine learning (Bayesian optimization) and thermal emission properties calculations (electromagnetic calculation). The joint team then experimentally fabricated the designed metamaterial and verified the performance. These results may facilitate the development of highly efficient energy devices.

Design and validation of world-class multilayered thermal emitter using machine learning

NIMS, the University of Tokyo, Niigata University and RIKEN have jointly designed a multilayered metamaterial that realizes ultra-narrowband wavelength-selective thermal emission by combining ...

Fri 15 Mar 19 from Phys.org

Design and validation of world-class multilayered thermal emitter using machine learning, Fri 15 Mar 19 from ScienceDaily

Design and validation of world-class multilayered thermal emitter using machine learning, Fri 15 Mar 19 from Eurekalert

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