The involvement of machine intelligence carries the process of design into a new association between two species which continuously influence each other during the search for an ideal design solution. Such design methodology is of high importance for a more integrated information rich and creative design thinking.
The research is focusing on development of multiple criteria search tools that can accommodate the ambiguous nature of the early design phase while keeping the awareness on design performance. The design development and communication can occur on different levels during the early design phase. The research intents to bring these design methods into a next level while keeping the act of design natural but extending it by integrating machine intelligence into the design process.
Early design phase needs a search strategy that supports the ambiguous nature of design, which can give the designer complete freedom of investigating the design instances with all their trade-offs, rather than limiting the designer with vaguely defined criteria to optimize for. Respectively, the research aims to develop an suitable design search strategy for this specific stage, that does not intend to find a mathematical optimum for a certain design problem, on the contrary supports the designer to find a desired solution by inspecting the trade-offs between different design criteria.
The project examines the integration of advanced modelling and heuristic search strategies to design workflow for managing and communicating multi-criteria search in the early design phase.It focuses on novel strategies for the formulation of design problems within the setting of high performance, cloud-enabled, multi-objective search. Accordingly within the frame work of well established platforms for parametric modelling different parametrisation methods are and novel use of methods from computer graphics, simulated physics and developmental biology are investigated.
ZEYNEP AKSÖZ is a PhD Candidate at the University of Applied Arts in Vienna in Institute of Architecture (IoA) http://i-o-a.at/. She is working on EU funded Research Project Innochain http://innochain.net/.
Her ongoing research topic is “Multiple Criteria Optimization in Early Design Phase”, where she focuses on the communication and integration of interdisciplinary… read more
Learning From Physical Data
Author: Zeynep Aksoz
AUTHOR: Zeynep Aksöz In this phase of the project the possibilities of working with physical design artifacts were investigated. The experiment was aiming to learn from the designers tacit knowledge by analyzing physical models and their performances created by the designers, to design new artifacts using artificial intelligence that base on the extracted rules... read more
Human Machine Interaction
The collaborative design strategy between human and artificial intelligence brings a new perspective into design thinking. Rather than using a top down strategy, where the designer searches for the preferred solution manually and optimizing the selected solution in a later step, s/he becomes the builder of the process, investigating the topological relations between the parameters... read more
ESR 4 – Multi-Criteria Optimization in Early Design Phase
Author: Zeynep Aksoz
ESR: Zeynep Aksoz ESR NUMBER: ESR4 INDUSTRIAL PARTNERS: BIG INSTITUTE: IOA DESCRIPTION: Multiple criteria optimization is a method to find best fitting solution for a complex problem depending on several objectives mainly by maximizing or minimizing a mathematical function. This method is commonly used in engineering practices aiming to find one optimal solution... read more
Her ongoing research topic is “Multiple Criteria Optimization in Early Design Phase”, where she focuses on the communication and integration of interdisciplinary information such as engineering performance, energy efficiency in the earlier design phases. read more