Efficient artificial intelligence techniques for inverse design and knowledge discovery in metamaterials

A new artificial-intelligence (AI) approach based on dimensionality reduction techniques for the design and knowledge discovery in metamaterial structures will be presented. It is shown that reducing the dimensionality of the response and design spaces by using a pseudoencoder can reduce the computation time by 2–3 orders of magnitude. In addition, using pruning techniques can simplify the trained algorithm to further reduce the computation time while providing simpler paths for relating input-output relationships for understanding the role of design parameters (i.e., knowledge discovery). It is also shown that this approach can enable an evolutionary design method in which the initial design can be evolved intelligently into an alternative with favorable specifications. The application of these AI approaches for both optical and acoustic metamaterials will be presented.