AI Design Optimization
Using machine learning to automatically improve designs for performance, cost, or efficiency.
Definition
AI Design Optimization employs machine learning algorithms to automatically improve building designs across multiple objectives simultaneously. Unlike traditional optimization that requires explicit mathematical formulations, AI can learn complex relationships from examples and find optimal solutions in high-dimensional design spaces. This enables optimization of building performance metrics like energy efficiency, structural efficiency, daylighting, and cost that would be impractical to optimize manually.
In Depth
Design optimization in AEC means balancing competing objectives — structural efficiency, energy performance, construction cost, material usage, and aesthetic intent — to find solutions that perform well across all criteria simultaneously. AI handles the mathematical complexity of multi-objective optimization that is impractical to do manually.
A practical example: optimizing a building's window-to-wall ratio. Larger windows improve daylighting and views but increase heating and cooling loads, structural loading on the facade, and construction cost. AI can evaluate thousands of facade configurations across all of these criteria and present the design team with a set of optimal trade-off options — "this configuration gives you 90% of the daylighting benefit at 70% of the energy cost" — so the architects make informed decisions rather than guessing.
The key is that AI optimization does not replace design judgment — it expands the solution space that designers can consider. Without AI, a designer might evaluate three or four facade options. With AI, they can evaluate hundreds and understand the performance implications of each, then select the option that best balances all project priorities.
Examples
Optimizing window placement for daylighting and energy efficiency simultaneously
Finding the most cost-effective structural system that meets all requirements
Automatically adjusting HVAC system sizing for optimal performance
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
AI Design Optimization employs machine learning algorithms to automatically improve building designs across multiple objectives simultaneously. Unlike traditional optimization that requires explicit mathematical formulations, AI can learn complex relationships from examples and find optimal solutions in high-dimensional design spaces. This enables optimization of building performance metrics like energy efficiency, structural efficiency, daylighting, and cost that would be impractical to optimize manually.
Optimizing window placement for daylighting and energy efficiency simultaneously. Finding the most cost-effective structural system that meets all requirements. Automatically adjusting HVAC system sizing for optimal performance.
Automated Drawing Review: Automatically review drawings against building codes, internal standards, and client requirements.