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Interactive generative design and multi-objective optimization

The first video shows the set-up and process of optimizing a simple parametric design for an neighborhood. The algorithm takes five fitness objectives into account: Solar comfort on the streets (weighted by pedestrian frequency); wind comfort; footfall through the neighborhood; access to the neighborhood, overall access to local transit stations; The latter two indicators are computed for the whole area, thus enabling to include a positive impact of the new quarter’s spatial arrangement on the whole neighborhood as a goal dimension. 

By using our deep learning based predictions for solar and wind related measures, one iteration takes just about three seconds to be computed.

The second video showcases a concept for a user interface focused on augmented reality environments and automatized optimization. The model parameters are interpolated between attractor points, each represented by a cube.
Significant parameters are related to the cube’s properties (such as position, height, width, angle etc.). Thus, allowing the user to intuitively interact with the model at any point before, in-between or after the optimization processes.

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