[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119063-en":3,"doc-seo-119063-105":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},119063,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning in Nonlinear Material Physics - SlideShare","Machine learning technologies are presented as accelerators for developing innovative materials, methods, and procedures in materials science, with emphasis on continuous materials mechanics. The overview connects automated learning and statistics to challenges in identifying, quantifying, and optimizing linked features across spatiotemporal scales. Applications are organized into descriptive, predictive, and prescriptive categories, considering factors such as data characteristics, scales, formats, and computational cost. Examples include use of neural-network architectures and additional data-driven tools to support prediction and optimization tasks.","Machine Learning in Nonlinear Material Physics  \nSaman Mohammed Abdulrahman 1*, Renas RajabAsaad1, Hawar Bahzad Ahmad1, Ahmed Alaa Hani2, Subhi R. M. Zeebaree3, Amira Bibo Sallow2  \n1 Department of Computer Science, College of Science,  \nNawroz University, Duhok, KRG, IRAQ  \n2 Department of Information Technology, Duhok Technical College,  \nDuhok Polytechnic University, Duhok, KRG, IRAQ  \n3 Energy Engineering Department, Technical College of Engineering,  \nDuhok Polytechnic University, Duhok, KRG, IRAQ  \n*Corresponding Author: [eman.khorsheed@nawroz.edu.krd](eman.khorsheed@nawroz.edu.krd)[ ](eman.khorsheed@nawroz.edu.krd)DOI: [https://doi.org/10.30880/jscdm.2024.05.01.010](https://doi.org/10.30880/jscdm.2024.05.01.010)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 1 December 2023 | Researchers and investors can accelerate the development of |\n| Accepted: 25 April 2024 | innovative materials, methods, and procedures by using machine |\n| Available online: 21 June 2024 | learning technologies. In materials science, one key objective of employing such methods is to make it easier to identify and quantify |\n| Keywords | high features throughout the chain of manipulation, organization, possessions, and efficiency. An overview of effective uses of automated |\n| Machine learning, data mining, | learning and statistics is given in this piece, which addresses specific |\n| continuum materials mechanics, | challenges in continuous materials mechanics. The classification of |\n| materials science, predictive | these applications is based on their nature, categorized as descriptive, |\n| modeling, prescriptive modeling, | predictive, or prescriptive, all aiming to identify, anticipate, or optimize |\n| neural networks, microstructure, | crucial attributes. The selection of the most suitable machine learning |\n| mechanical properties, performance | technique is influenced by factors such as the unique use case, content |\n| evaluation | type, data characteristics, geographical and temporal scales, formats, targeted knowledge gain, and affordable computing expenses. Various examples are explored, including using various artificially generated share network architectures on an as-needed basis in conjunction with additional data-driven approaches such as basic constituent assessment, decisions shrubs, models, woods, trees, supported matrix, and Gaussian learners. |\n\n1. Introduction  \nThe application of machine learning techniques in continuous materials mechanics is driven by the potential to expedite and streamline the discovery and development of new materials for future applications [1] . The significant challenge lies in manipulating material qualities to achieve a desirable blend of features and performance attributes. The primary goals in developing materials for specific applications involve detecting linked physical events across various spatiotemporal scales, addressing statistical errors, and regulating the parameter space within materials structures [2] . It is crucial to consider the statistical variation of the current process to connect the impacts of process settings to microstructural traits, material qualities, and performance characteristics across different scales. Additionally, data mining assists scientists in exploring and comprehending intricate nonlinear interactions at a fundamental level [3-6] .  \nData mining and machine learning techniques are frequently employed as stepping stones in addressing complex issues until the nature of the connection of interest can be encapsulated by more general physical models replacing the learned algorithms. There is exceptional potential to objectively calibrate unexpected model forms  \nand parameters in physics-based models through machine learning techniques grounded in rigorous statistical methods [7] . Methodologically, the fields of machine learning and data mining, components of the data science toolkit, are closely connected to applied statistics, and their distinctions ","cbCaimXzUzutSl4S","https://ap.wps.com/l/cbCaimXzUzutSl4S","pdf",644403,1,10,"English","en",105,"# Introduction\n## Data mining and machine learning in materials mechanics\n## Process-structure-property-performance chain\n## Classification of applications and learning types\n# Overview of techniques and applications\n## Descriptive, predictive, and prescriptive goals\n## Factors affecting technique selection","[{\"question\":\"How does machine learning support discovery of new materials in continuous materials mechanics?\",\"answer\":\"It helps expedite and streamline identifying material qualities that achieve desired blends of features and performance across spatiotemporal scales, while addressing statistical errors and parameter regulation.\"},{\"question\":\"What are the main categories used to classify machine learning applications in the document?\",\"answer\":\"Applications are grouped by application fields—performance, microstructure, mechanical characteristics, and process parameters—and further classified into descriptive, predictive, and prescriptive tasks.\"},{\"question\":\"What factors influence the selection of the most suitable machine learning technique?\",\"answer\":\"The document highlights the specific use case type, data characteristics, geographical and temporal scales, data formats, targeted knowledge gain, and affordable computing expenses.\"}]","Machine Learning in Nonlinear Material Physics - 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