Ruiz Vargas, Jose Cupertino [UNESP]Novaes, Sergio Ferraz [UNESP]Cobe, Raphael [UNESP]Iope, Rogerio [UNESP]Stanzani, Silvio Luiz [UNESP]Tomei, Thiago [UNESP]2019-10-062019-10-062018-10-01Proceedings - 2018 44th Latin American Computing Conference, CLEI 2018, p. 294-298.http://hdl.handle.net/11449/190600Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength.294-298engFresnel diffractionMachine LearningTensorflowVariational AutoencodersTrabalho apresentado em evento10.1109/CLEI.2018.00043Acesso restrito2-s2.0-85071121316