[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118938-en":3,"doc-seo-118938-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118938,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions","A comprehensive machine-learning framework characterizes labyrinthine structures that arise as steady states in pattern-forming systems. Rotation-invariant template matching identifies local stripe-order topological defects, followed by convolutional neural network analysis to determine defect types and coordinates in image data. Applied to single-crystal Bi-substituted yttrium iron garnet films, the method reveals a distinct morphological transition between two zero-field labyrinthine states. Pair distribution functions of the defects expose subtle differences beyond conventional structure-factor characterization, enabling new insights into athermal dynamics and spatial correlations that govern the observed transitions.","Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions  \narXiv :2311 . 10558v2 [ cond-mat .soft] 28 Oct 2024  \nKotaro Shimizu, 1, 2 Vinicius Yu Okubo,3 Rose Knight, 1 Ziyuan Wang, 1 Joseph Burton, 1 Hae Yong Kim,3 Gia-Wei Chern, 1 and B. S. Shivaram 1  \n1 Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA  \n2 Department of Applied Physics, The University of Tokyo, Tokyo 113-8656, Japan  \n3 Dept. Electronic Systems Engineering, Polytechnic School, University of São Paulo, Brazil (Dated: October 29, 2024)  \nWe present a comprehensive approach to characterizing labyrinthine structures that often emerge as a final steady state in pattern forming systems. We employ advanced machine learning based pattern recognition techniques to identify the types and locations of topological defects of the local stripe ordering. Applying this method to single-crystal Bi-substituted Yttrium Iron Garnet films, we uncover a distinct morphological transition between two zero-field labyrinthine structures.Crucially, the pair distribution functions of the topological defects reveal subtle differences between labyrinthine structures which are beyond conventional characterization methods. By systematically analyzing the spatial correlations and geometric properties of these defects, we provide new insights into theathermal dynamics governing the observed morphological transitions. Our work demonstrates that machine learning based recognition techniques enable novel studies of rich and complex labyrinthine type structures universal to many pattern formation systems.  \nLabyrinthine structures are ubiquitous in out-ofequilibrium nonlinear systems ranging from biological and chemical reactions to fluid convection, crystal growth, and magnetic ordering [1–5] . In such pattern forming systems, the complex structures emerge as a result of competing interactions in a highly nonlinear way. The labyrinthine patterns are generally characterized by stripe domains of different orientations, sizes, and grain-boundary structures. The predominance of periodic stripes indicates breaking of translational symmetry locally. Yet, contrary to long-range ordered states in an equilibrium phase transition, labyrinthine patterns are essentially disordered and cannot be described by a well-defined order parameter. Indeed, labyrinthine structures can be viewed as intermediate between a featureless short-range correlated glassy state and long-range ordered stripe or crystalline phases [6] .  \nDespite their prevalence in pattern forming systems, a complete characterization of labyrinthine structures is still lacking [7] . A defining characteristic of labyrinthine patterns is the ring-like feature in its structure factor obtained from conventional Fourier analysis [8] . The radius and width of the ring correspond to the wavelength of local stripes and characteristic size of stripe domains, respectively [8–11] . While such global Fourier analysis provides a basic characterization of labyrinths, it fails to capture subtle differences of labyrinthine patterns which have important structural or dynamical implications. Other useful measures, such as the disorder functions [12–14], have been introduced to quantify deviations from a perfect stripe order. Another important characterization often employed is the density of topological defects of labyrinthine structures [15–18] . Indeed, the distribution and correlation between topological defects, such as disclinations and dislocations, of the stripe order encode important information about the  \nlabyrinths [17, 18] . However, efficient and accurate identifications of such point-like defects in large-scale experimental or simulation data remain a challenging task.  \nIn this paper, we present a comprehensive framework for the characterization of labyrinthine structures by leveraging machine learning based template recognition methods. A two-step algorithm that consists of rotationinvarian","cbCaimJSyw7tW4x4","https://ap.wps.com/l/cbCaimJSyw7tW4x4","pdf",3155143,1,6,"English","en",105,"# Introduction\n## Background on labyrinthine structures\n## Limitations of conventional characterization\n# Method\n## Two-step defect identification algorithm\n## Real-space defect configuration analysis\n# Application\n## Study of YIG film morphological phase transition\n## Bi-doped YIG and magnetic domain imaging\n# Results and insights\n## Defect pair distributions and morphological differences\n## Athermal dynamics interpretation","[{\"question\":\"What problem does the paper address in characterizing labyrinthine patterns?\",\"answer\":\"It addresses the lack of complete characterization methods for labyrinthine structures and the difficulty of efficiently and accurately identifying point-like topological defects in large data sets.\"},{\"question\":\"How does the proposed machine-learning framework identify topological defects?\",\"answer\":\"It uses a two-step approach: rotation-invariant template matching followed by convolutional neural network analysis to classify defect types and extract their coordinates.\"},{\"question\":\"What new information do pair distribution functions of topological defects provide?\",\"answer\":\"They reveal subtle differences between labyrinthine structures that are not captured by conventional Fourier/structure-factor characterization, offering insights into the athermal dynamics behind morphological transitions.\"}]","Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions | 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problem does the paper address in characterizing labyrinthine patterns?","Question",{"text":75,"@type":76},"It addresses the lack of complete characterization methods for labyrinthine structures and the difficulty of efficiently and accurately identifying point-like topological defects in large data sets.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed machine-learning framework identify topological defects?",{"text":80,"@type":76},"It uses a two-step approach: rotation-invariant template matching followed by convolutional neural network analysis to classify defect types and extract their coordinates.",{"name":82,"@type":73,"acceptedAnswer":83},"What new information do pair distribution functions of topological defects provide?",{"text":84,"@type":76},"They reveal subtle differences between labyrinthine structures that are not captured by conventional Fourier/structure-factor characterization, offering insights into the athermal dynamics behind morphological 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