The invention relates to the technical field of
artificial intelligence and
metamaterial research, in particular to a
metamaterial reverse design method and
system meeting multiple conditions. The invention provides a
metamaterial reverse design method meeting multiple conditions, which comprises the following steps of: firstly, generating a metamaterial unit
cell picture by using an initial variable combination randomly generated in a range, and then inputting the picture into a trained prediction model to obtain a dispersion graph and
transmission loss data. And finally, substituting the prediction result into the
fitness function, if the requirement is met, outputting the variable and the prediction result, otherwise, applying penalty to carry out iteration until the maximum number of iterations is reached. According to the invention, rapid design of the metamaterial is realized, the designed metamaterial has a
wide band gap, and ideal target
band gap and
transmission loss conditions can be obtained; according to the invention, the
deep learning model is utilized to meet the metamaterial reverse design of the required
band gap and
transmission loss conditions, and the multi-target
performance requirement is met. According to the method, the problems that in the prior art, a traditional metamaterial design method depends on experience-driven iterative optimization and is limited by calculation efficiency, high-dimensional parameter space is difficult to efficiently explore, and complex multi-target performance requirements are difficult to meet are solved.