Deep Learning in Computational Design Synthesis: A Comprehensive Review
Shubhendu Singh, Rahul Rai, Raj Pradip Khawale, Darshil Patel, Dustin Bielecki, Ryan Nguyen, Jun Wang, Zhibo Zhang
Abstract
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.