Deep Learning in Computational Design Synthesis: A Comprehensive ReviewSource: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 004::page 40801-1Author:Kumar Singh, Shubhendu
,
Rai, Rahul
,
Pradip Khawale, Raj
,
Patel, Darshil
,
Bielecki, Dustin
,
Nguyen, Ryan
,
Wang, Jun
,
Zhang, Zhibo
DOI: 10.1115/1.4064215Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A paradigm shift in the computational design synthesis (CDS) 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 article 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 CDS process, which consists of four submodules: representation, generation, evaluation, and guidance. Each submodule has undergone an analysis to identify potential research gaps and formulate research questions. In addition, we consider the limitations of our study and pinpoint the realms where the research can be extended in the future.
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contributor author | Kumar Singh, Shubhendu | |
contributor author | Rai, Rahul | |
contributor author | Pradip Khawale, Raj | |
contributor author | Patel, Darshil | |
contributor author | Bielecki, Dustin | |
contributor author | Nguyen, Ryan | |
contributor author | Wang, Jun | |
contributor author | Zhang, Zhibo | |
date accessioned | 2024-04-24T22:32:37Z | |
date available | 2024-04-24T22:32:37Z | |
date copyright | 1/8/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 1530-9827 | |
identifier other | jcise_24_4_040801.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295416 | |
description abstract | A paradigm shift in the computational design synthesis (CDS) 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 article 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 CDS process, which consists of four submodules: representation, generation, evaluation, and guidance. Each submodule has undergone an analysis to identify potential research gaps and formulate research questions. In addition, we consider the limitations of our study and pinpoint the realms where the research can be extended in the future. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Deep Learning in Computational Design Synthesis: A Comprehensive Review | |
type | Journal Paper | |
journal volume | 24 | |
journal issue | 4 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4064215 | |
journal fristpage | 40801-1 | |
journal lastpage | 40801-26 | |
page | 26 | |
tree | Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 004 | |
contenttype | Fulltext |