Owl

PAACADEMY offers advanced online courses on the Owl Grasshopper plugin, teaching computational designers how to integrate machine learning and data-driven prediction models directly into parametric workflows.

Owl is a Grasshopper plugin that integrates machine learning algorithms into parametric design workflows, enabling designers to train models, predict design outcomes, and optimize parametric systems using data-driven approaches without leaving the Grasshopper environment. Its position in a design workflow is at the optimization and learning stage: after a parametric system has been built in Grasshopper, Owl is used to understand the massive datasets that system generates.

At PAACADEMY, Owl is taught in Optimizing Design Decisions With Machine Learning by Zvonko Vugreshek. This course bridges the gap between geometry and data science:

• Model Training: Training supervised and unsupervised machine learning models on architectural geometry data directly on the Grasshopper canvas.
• Predictive Optimization: Using ML models to predict structural or environmental performance without running computationally heavy simulations.
• Morphology Analysis: Discovering hidden patterns in geometric datasets to inform smarter, automated design decisions.