scholarly journals Impact of Design Representations on Creativity of Design Outcomes

2020 ◽  
Vol 23 (2) ◽  
pp. 31-60 ◽  
Author(s):  
Gaetano Cascini ◽  
Lorenzo Fiorineschi ◽  
Federico Rotini
2018 ◽  
Vol 10 (2) ◽  
pp. 1-146 ◽  
Author(s):  
Marco Attene ◽  
Marco Livesu ◽  
Sylvain Lefebvre ◽  
Thomas Funkhouser ◽  
Szymon Rusinkiewicz ◽  
...  

2019 ◽  
Vol 147 (5) ◽  
pp. 1713-1731 ◽  
Author(s):  
Marlene Baumgart ◽  
Paolo Ghinassi ◽  
Volkmar Wirth ◽  
Tobias Selz ◽  
George C. Craig ◽  
...  

Abstract Two diagnostics based on potential vorticity and the envelope of Rossby waves are used to investigate upscale error growth from a dynamical perspective. The diagnostics are applied to several cases of global, real-case ensemble simulations, in which the only difference between the ensemble members lies in the random seed of the stochastic convection scheme. Based on a tendency equation for the enstrophy error, the relative importance of individual processes to enstrophy-error growth near the tropopause is quantified. After the enstrophy error is saturated on the synoptic scale, the envelope diagnostic is used to investigate error growth up to the planetary scale. The diagnostics reveal distinct stages of the error growth: in the first 12 h, error growth is dominated by differences in the convection scheme. Differences in the upper-tropospheric divergent wind then project these diabatic errors into the tropopause region (day 0.5–2). The subsequent error growth (day 2–14.5) is governed by differences in the nonlinear near-tropopause dynamics. A fourth stage of the error growth is found up to 18 days when the envelope diagnostic indicates error growth from the synoptic up to the planetary scale. Previous ideas of the multiscale nature of upscale error growth are confirmed in general. However, a novel interpretation of the governing processes is provided. The insight obtained into the dynamics of upscale error growth may help to design representations of uncertainty in operational forecast models and to identify atmospheric conditions that are intrinsically prone to large error amplification.


Author(s):  
Dennis C. Neale ◽  
Jonathan K. Kies

Scenario-based techniques have been receiving increased attention in the design of human-computer interaction. A cohesive methodology or framework, however, has yet to materialize, and scenario methods have not been well defined. Claims are being made about the ability of scenarios to play a role throughout the development life cycle. The objective of this paper is to examine the ability of scenarios to serve as the primary design representations early in the system design life cycle for envisioning the system, requirements specification, user-designer communication, and design rationale. These findings represent a case study in the design of a world-wide web site for the Human Factors Engineering Center at Virginia Tech. Example-based narratives were elicited using a “micro-scenario” generating task that involved prospective end-users brainstorming user-system interactions. Conclusions are drawn about the effectiveness of the technique for system development, and guidelines are provided for using scenarios to specify behavioral requirements.


Author(s):  
Somwrita Sarkar ◽  
Andy Dong ◽  
John S. Gero

AbstractAn artificial intelligence (AI) algorithm to automate symbolic design reformulation is an enduring challenge in design automation. Existing research shows that design tools either require high levels of knowledge engineering or large databases of training cases. To address these limitations, we present a singular value decomposition (SVD) and unsupervised clustering-based method that performs design reformulation by acquiring semantic knowledge from the syntax of design representations. The development of the method was analogically inspired by applications of SVD in statistical natural language processing and digital image processing. We demonstrate our method on an analytically formulated hydraulic cylinder design problem and an aeroengine design problem formulated using a nonanalytic design structure matrix form. Our results show that the method automates various design reformulation tasks on problems of varying sizes from different design domains, stated in analytic and nonanalytic representational forms. The behavior of the method presents observations that cannot be explained by pure symbolic AI approaches, including uncovering patterns of implicit knowledge that are not readily encoded as logical rules, and automating tasks that require the associative transformation of sets of inputs to experiences. As an explanation, we relate the structure and performance of our algorithm with findings in cognitive neuroscience, and present a set of theoretical postulates addressing an alternate perspective on how symbols may interact with each other in experiences to reify semantic knowledge in design representations.


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