Gramazio Kohler Research

AI-eXtended Design is open-source Python toolbox designed to revolutionize parametric design with the power of modern Machine Learning (ML) algorithms. AIXD is your go-to toolkit for design evaluation, generation, exploration, and optimization, seamlessly blending parametric modeling with AI capabilities.

Key Features:

Generative Power: AIXD empowers you to easily train generative AI models, such as Conditional Autoencoders. This enables the creation of designs that not only push the boundaries of creativity but also meet the specific performance constraints of your project, advancing design exploration.

Surrogate Modeling: Easily create surrogate models for parametric problems using feed-forward neural networks, enhancing efficiency in design evaluation and optimization.

Versatility: Initially developed for the Architecture, Engineering, and Construction (AEC) industry, AIXD is applicable to any parametric problem across diverse domains.

Project-Specific and Low-Code: Tailor AIXD to your project’s unique needs with its project-specific approach, all while enjoying the benefits of a low-code environment that accelerates your design workflow.

AIXD website including API reference, user guide, tutorials and source code.

Related projects:

AI-Augmented Architectural Design

Architectural Design with Conditional Autoencoders: Semiramis Case Study

Credits:

Gramazio Kohler Research, ETH Zurich
Prof. Matthias Kohler, Dr. Aleksandra Apolinarska, Gonzalo Casas, Dr. Romana Rust

Swiss Data Science Center (SDSC)
Dr. Luis Salamanca, Alessandro Maissen, Rafael Bischof, Dr. Konstantinos Tatsis, Prof. Fernando Perez-Cruz

kfm research, ETH Zurich
Prof. Walter Kaufmann, Dr. Michael Kraus, Sophia Kuhn


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