Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network, support vector machine and fuzzy logic

• Published in: Neural computing & applications Vol. 29; no. 12; pp. 1583 - 1591
• Main Authors: Abdolrazzaghi, Mohammad, Hashemy, Soheil, Abdolali, Ali
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Differential equations; Fuzzy logic; Image Processing and Computer Vision; Inhomogeneous media; Learning theory; Machine learning; Neural networks; Original Article; Probability and Statistics in Computer Science; Support vector machines; Fuzzy logic; Inhomogeneous media; Artificial neural network; Support vector machine; Scattering
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2694-9

Encountering with a nonlinear second-order differential equation including ϵ r and μ r spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of ϵ r and μ r with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.