CityMatrix was introduced to address these challenges. Machine learning techniques were applied to achieve real-time prediction of multiple urban simulations, and thousands of city configurations were simulated. The simulation results were used to train a convolutional neural network (CNN) to predict the traffic and solar performance of unseen city configurations. The prediction with the CNN is thousands of times faster than the original simulations and maintains a high-quality representation of the results. This machine learning approach was applied as a versatile, quick, accurate, and computationally efficient method not only for real-time feedback, but also for optimized design recommendations. Users involved in the evaluation of this project had a better understanding of the embodied trade-offs of the city and achieved their goals in an efficient manner.

In the paper, we describe the algorithms of the computational evaluation method. We also show how it can be used to introduce fabrication considerations into the design process by using it to rationalize several types of panels. Additionally, we demonstrate how the method can be used in complex, large-scale architectural projects to save machining time and materials by evaluating and altering the paneling subdivision.