@Article{SautterRosaFerr:2023:GrPaAn,
               author = "Sautter, Rubens Andreas and Rosa, Reinaldo Roberto and Ferreira, 
                         Luan Oriomn de Oliveira Bara{\'u}na",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Gradient Pattern Analysis of Coupled Map Lattices:Insights into 
                         Transient and Long-Term Behaviors",
              journal = "Proceeding Series of the Brazilian Society of Computational and 
                         Applied Mathematics",
                 year = "2023",
               volume = "10",
               number = "1",
                pages = "e10058",
                 note = "Trabalho apresentado no XLII CNMAC, Universidade Federal de Mato 
                         Grosso do Sul - Bonito - MS, 2023.",
             keywords = "Coupled Map Lattice (CML), Gradient Pattern Analysis (GPA), 
                         Chaotic systems.",
             abstract = "Gradient Pattern Analysis (GPA) is a useful technique for 
                         analyzing the dynamics ofnonlinear 2D-spatiotemporal systems, 
                         which is based on the gradient symmetry-breaking propertiesof a 
                         matrix snapshot sequence. GPA has found numerous applications in 
                         dynamic systems, particu-larly in studying logistic Coupled Map 
                         Lattices (CMLs) and Swift-Hohenberg amplitude equations.In this 
                         work, we propose a new mathematical operation related to the first 
                         gradient moment (G1)defined by the GPA theory. The performance of 
                         this new measure is evaluated by applying it totwo chaotic CML 
                         models (Logistic and Shobu-Ose-Mori). The GPA using the new 
                         parameter (G1)provides a more accurate analysis, allowing the 
                         identification of conditions that partially break thegradient 
                         symmetry over time. Based on the GPA measurements (G1,G2andG3), 
                         including acombined analysis with the chaotic parameters, our 
                         results demonstrate the potential to analyzechaotic spatiotemporal 
                         systems improving our understanding of their underlying 
                         dynamics.",
                  doi = "10.5540/03.2023.010.01.0058",
                  url = "http://dx.doi.org/10.5540/03.2023.010.01.0058",
                 issn = "2359-0793",
             language = "en",
           targetfile = "4172-Texto do Artigo-8188-8373-10-20231109.pdf",
        urlaccessdate = "16 maio 2024"
}