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Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible
Author(s) -
Bessa Miguel A.,
Glowacki Piotr,
Houlder Michael
Publication year - 2019
Publication title -
advanced materials
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201904845
Subject(s) - microscale chemistry , metamaterial , materials science , code (set theory) , material design , engineering design process , base (topology) , process (computing) , space (punctuation) , optimal design , brittleness , bayesian probability , computer science , bayesian optimization , mechanical engineering , nanotechnology , artificial intelligence , machine learning , composite material , optoelectronics , mathematics , programming language , engineering , operating system , mathematical analysis , mathematics education , set (abstract data type)