TY - GEN
T1 - Using deep learning to simulate multi-disciplinary design teams
AU - Stump, Gary M.
AU - Yukish, Michael
AU - Cagan, Jonathan
AU - McComb, Christopher
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2021
Y1 - 2021
N2 - Human subject experiments are often used in research efforts to understand human behavior in design. However, such research is often time-consuming, expensive, and limited in scope due to the need to experimentally control specific variables. This work develops an initial digital simulation of team-based multidisciplinary design, where the actions of individual team members are simulated using deep learning models trained on historical human design trends. The main benefit of this work is to simulate design session events and interactions without human participants, developing a complimentary method to rapidly perform digital team-based experiments. This research merges the benefits of purely data-driven modeling with minimal assumptions about process, along with the strengths of agent-based modeling in which it is possible to tailor agent behavior. Initial results show that the simulated design team sessions are able to replicate trends and distributions compared to human-based team sessions, but run approximately 21 times faster than equivalent human subject studies. The multi-disciplinary design problem currently simulated is loosely coupled, in the sense that agent behaviors can be modeled in isolation of other agents and yet replicate the behavior of the ensemble. Future work will extend the agents to sense and respond behaviors that can be used to model tightly coupled problems, and truly evaluate team formulations.
AB - Human subject experiments are often used in research efforts to understand human behavior in design. However, such research is often time-consuming, expensive, and limited in scope due to the need to experimentally control specific variables. This work develops an initial digital simulation of team-based multidisciplinary design, where the actions of individual team members are simulated using deep learning models trained on historical human design trends. The main benefit of this work is to simulate design session events and interactions without human participants, developing a complimentary method to rapidly perform digital team-based experiments. This research merges the benefits of purely data-driven modeling with minimal assumptions about process, along with the strengths of agent-based modeling in which it is possible to tailor agent behavior. Initial results show that the simulated design team sessions are able to replicate trends and distributions compared to human-based team sessions, but run approximately 21 times faster than equivalent human subject studies. The multi-disciplinary design problem currently simulated is loosely coupled, in the sense that agent behaviors can be modeled in isolation of other agents and yet replicate the behavior of the ensemble. Future work will extend the agents to sense and respond behaviors that can be used to model tightly coupled problems, and truly evaluate team formulations.
UR - http://www.scopus.com/inward/record.url?scp=85120006603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120006603&partnerID=8YFLogxK
U2 - 10.1115/DETC2021-70596
DO - 10.1115/DETC2021-70596
M3 - Conference contribution
AN - SCOPUS:85120006603
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 47th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - 47th Design Automation Conference, DAC 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021
Y2 - 17 August 2021 through 19 August 2021
ER -