Training artificial neural network for optimization of nanostructured VO2-based smart window performance

Igal Balin, Valery Garmider, Yi Long, Ibrahim Abdulhalim

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

In this work, we apply for the first time a machine learning approach to design and optimize VO2 based nanostructured smart window performance. An artificial neural network was trained to find the relationship between VO2 smart window structural parameters and performance metrics-luminous transmittance (Tlum) and solar modulation (ΔTsol), calculated by first-principle electromagnetic simulations (FDTD method). Once training was accomplished, the combination of optimal Tlum and ΔTsol was found by applying classical trust region algorithm on the trained network. The proposed method allows flexibility in definition of the optimization problem and provides clear uncertainty limits for future experimental realizations.

Original languageEnglish
Pages (from-to)A1030-A1040
JournalOptics Express
Volume27
Issue number16
DOIs
StatePublished - 5 Aug 2019

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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