MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation

Adam Sebestyen

MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation

Institute of Architecture and Media (1610)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Original language	English
Title of host publication	Divergence in Architectural Research
Subtitle of host publication	Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology
Publisher	Georgia Tech
ISBN (Print)	978-0-9615650-1-5
Publication status	Published - Feb 2021
Event	1st ConCave Ph.D. Symposium Divergence in Archtiectural Research - Georgia Tech, Atlanta, United States Duration: 5 Mar 2020 → 6 Mar 2020

Conference

Conference	1st ConCave Ph.D. Symposium Divergence in Archtiectural Research
Country/Territory	United States
City	Atlanta
Period	5/03/20 → 6/03/20

Cite this

Sebestyen, A. (2021). MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation. In Divergence in Architectural Research: Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology Georgia Tech.

MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation. / Sebestyen, Adam.
Divergence in Architectural Research: Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology. Georgia Tech, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Sebestyen, A 2021, MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation. in Divergence in Architectural Research: Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology. Georgia Tech, 1st ConCave Ph.D. Symposium Divergence in Archtiectural Research, Atlanta, United States, 5/03/20.

Sebestyen A. MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation. In Divergence in Architectural Research: Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology. Georgia Tech. 2021

Sebestyen, Adam. / MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS : Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation. Divergence in Architectural Research: Proceeding Book of ConCave Ph.D. Symposium Georgia Institute of Technology. Georgia Tech, 2021.

@inproceedings{d9cfb60e953047d5b67f65dc38f467fd,

title = "MACHINE LEARNING FOR ARCHITECTS AND DESIGNERS: Implementing Machine Learning into the Digital Design Process in Order to Speed Up Work Time within Parametric Design Workflows and Exploring Methods of New Workflow and Design Output Creation",

author = "Adam Sebestyen",

year = "2021",

month = feb,

language = "English",

isbn = "978-0-9615650-1-5",

booktitle = "Divergence in Architectural Research",

publisher = "Georgia Tech",

note = "1st ConCave Ph.D. Symposium Divergence in Archtiectural Research ; Conference date: 05-03-2020 Through 06-03-2020",

}