Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Texto completo
Autor(es):
Sigaki, H. Y. D. [1] ; de Souza, R. F. [1] ; de Souza, R. T. [1, 2] ; Zola, R. S. [1, 2] ; Ribeiro, V, H.
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] V, Univ Estadual Maringa, Dept Fis, BR-87020900 Maringa, Parana - Brazil
[2] Univ Tecnol Fed Parana, Dept Fis, BR-86812460 Apucarana, PR - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Physical Review E; v. 99, n. 1 JAN 22 2019.
Citações Web of Science: 0
Resumo

Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of that, considerably less attention has been paid to the problem of extracting physical properties of liquid crystals directly from textures images of these materials. Here we present an approach that combines two physics-inspired image quantifiers (permutation entropy and statistical complexity) with machine learning techniques for extracting physical properties of nematic and cholesteric liquid crystals directly from their textures images. We demonstrate the usefulness and accuracy of our approach in a series of applications involving simulated and experimental textures, in which physical properties of these materials (namely: average order parameter, sample temperature, and cholesteric pitch length) are predicted with significant precision. Finally, we believe our approach can be useful in more complex liquid crystal experiments as well as for probing physical properties of other materials that are investigated via imaging techniques. (AU)

Processo FAPESP: 14/50983-3 - INCT 2014: fluidos complexos
Beneficiário:Antonio Martins Figueiredo Neto
Linha de fomento: Auxílio à Pesquisa - Temático