|PI: Sai-Kit Yeung||Funding Source: SUTD-IDC|
|Co PI: -||Start Date: 1 January, 2013|
|Research Areas: Computational Design, Computer Graphics||End Date: 9 January, 2016|
We propose a learning-optimization paradigm to scientifically facilitate various design processes. The core idea is to propose a paradigm by making use of machine learning and optimization methods to encode, facilitate and automate the underlying design processes. The fundamental contribution will be its novel two-steps approach, which is adaptable, scalable and extensible over any ad-hoc rules-based or labeled interval calculus approach. The learning component is useful to capture and encode the abstract rules of the given design problem and the optimization component allows efficient computation of multiple optimal solutions. It will be very much similar to the design principle of some actual design processes where the designers have their prior domain knowledge and sample between multiple design solutions in their brain based on their knowledge.