Convolutional Neural Networks (CNNs) can learn to distinguish these boundaries for metamaterials in minute detail. This raises the possibility of complex material design by indicating that the network infers the underlying combinatorial rules from the sparse training set. Researchers from the UvA Institute of Physics and the research center AMOLF have shown that researchers may more effectively and precisely respond to such queries by using machine learning techniques. The team’s recent study examined the accuracy of forecasting the characteristics of these characteristics using artificial intelligence. The accuracy of the predictions showed that the neural networks had truly mastered the fundamental mathematical principles governing the behavior of metammaterials, which still need to be better understood by researchers. These results indicate that complicated . . .
Read more at www.marktechpost.com
