S. Singh, Rahul Rai, Raj Pradip Khawale, Darshil Patel, Dustin Bielecki, Ryan Nguyen, Jun Wang, Zhibo Zhang
{"title":"Deep Learning in Computational Design Synthesis: A Comprehensive Review","authors":"S. Singh, Rahul Rai, Raj Pradip Khawale, Darshil Patel, Dustin Bielecki, Ryan Nguyen, Jun Wang, Zhibo Zhang","doi":"10.1115/1.4064215","DOIUrl":null,"url":null,"abstract":"\n A paradigm shift in the computational design synthesis domain is being witnessed by the onset of the innovative usage of machine learning techniques. The rapidly evolving paradigmatic shift calls for systematic and comprehensive assimilation of extant knowledge at the intersection of machine learning and computational design synthesis. Understanding nuances, identifying research gaps, and outlining the future direction for cutting-edge research is imperative. This paper outlines a hybrid literature review consisting of a thematic and framework synthesis survey to enable conceptual synthesis of information at the convergence of computational design, machine learning, and big-data models. The thematic literature survey aims at conducting an in-depth descriptive survey along the lines of a broader theme of machine learning in computational design. The framework synthesis-based survey tries to encapsulate the research findings in a conceptual framework to understand the domain better. The framework is based on the computational design synthesis (CDS) process, which consists of four sub-modules: representation, generation, evaluation, and guidance. Each sub-module has undergone an analysis to identify potential research gaps and formulate research questions. Additionally, we consider the limitations of our study and pinpoint the realms where the research can be extended in the future.","PeriodicalId":54856,"journal":{"name":"Journal of Computing and Information Science in Engineering","volume":"31 7","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computing and Information Science in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4064215","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A paradigm shift in the computational design synthesis domain is being witnessed by the onset of the innovative usage of machine learning techniques. The rapidly evolving paradigmatic shift calls for systematic and comprehensive assimilation of extant knowledge at the intersection of machine learning and computational design synthesis. Understanding nuances, identifying research gaps, and outlining the future direction for cutting-edge research is imperative. This paper outlines a hybrid literature review consisting of a thematic and framework synthesis survey to enable conceptual synthesis of information at the convergence of computational design, machine learning, and big-data models. The thematic literature survey aims at conducting an in-depth descriptive survey along the lines of a broader theme of machine learning in computational design. The framework synthesis-based survey tries to encapsulate the research findings in a conceptual framework to understand the domain better. The framework is based on the computational design synthesis (CDS) process, which consists of four sub-modules: representation, generation, evaluation, and guidance. Each sub-module has undergone an analysis to identify potential research gaps and formulate research questions. Additionally, we consider the limitations of our study and pinpoint the realms where the research can be extended in the future.
期刊介绍:
The ASME Journal of Computing and Information Science in Engineering (JCISE) publishes articles related to Algorithms, Computational Methods, Computing Infrastructure, Computer-Interpretable Representations, Human-Computer Interfaces, Information Science, and/or System Architectures that aim to improve some aspect of product and system lifecycle (e.g., design, manufacturing, operation, maintenance, disposal, recycling etc.). Applications considered in JCISE manuscripts should be relevant to the mechanical engineering discipline. Papers can be focused on fundamental research leading to new methods, or adaptation of existing methods for new applications.
Scope: Advanced Computing Infrastructure; Artificial Intelligence; Big Data and Analytics; Collaborative Design; Computer Aided Design; Computer Aided Engineering; Computer Aided Manufacturing; Computational Foundations for Additive Manufacturing; Computational Foundations for Engineering Optimization; Computational Geometry; Computational Metrology; Computational Synthesis; Conceptual Design; Cybermanufacturing; Cyber Physical Security for Factories; Cyber Physical System Design and Operation; Data-Driven Engineering Applications; Engineering Informatics; Geometric Reasoning; GPU Computing for Design and Manufacturing; Human Computer Interfaces/Interactions; Industrial Internet of Things; Knowledge Engineering; Information Management; Inverse Methods for Engineering Applications; Machine Learning for Engineering Applications; Manufacturing Planning; Manufacturing Automation; Model-based Systems Engineering; Multiphysics Modeling and Simulation; Multiscale Modeling and Simulation; Multidisciplinary Optimization; Physics-Based Simulations; Process Modeling for Engineering Applications; Qualification, Verification and Validation of Computational Models; Symbolic Computing for Engineering Applications; Tolerance Modeling; Topology and Shape Optimization; Virtual and Augmented Reality Environments; Virtual Prototyping