Genesis Laboratory of Generative Systems and Sciences


Genesis Laboratory of Geneterative Systems and Sciences (Genesis Lab) is an independent laboratory for researching and developing physical, mathematical, and computational modules for generative design-build systems and design games as well as research, development, and activism in the area of generative sciences (i.e. simulation-driven complexity sciences), particularly focused on Collective Intelligence and Co-Creation. The lab focuses on digital design applications ranging from design optimization to industrial mass-customization in use cases such as affordable quality housing.

Visit the website of Genesis Lab for more information.

Visiting Address:

We are currently moving the lab from Delft to the Hague or Rotterdam; stay tuned online!

Our research output is continuously shared on ResearchGate.

Genesis Lab is a new research collaboratorium initiated by myself and my friend and colleague Ir. Shervin Azadi for streamlining our research and development agendas around the axis of generative systems and generative sciences with the aim of developing generative design methodologies.

We have released an early version of the core of our technical infrastructure for research in Genesis Lab as an open-source python/numpy library:

topoGenesis: an open-source python package that provides topological structures and functions for Generative Systems and Sciences for various application areas such as:

  • generative design in architecture and built environment

  • generative spatial simulations

  • 3D image processing

  • topological data analysis

  • machine learning

topoGenesis aims to utilize the vast functionalities of fields (mathematical objects) in generative systems and sciences. Therefore it seeks to:

  1. offer basic mathematical functionalities on field data models

  2. offer functionalities of computational topology on top of the field structures

  3. facilitate the conversion between mesh-based data models and field data models.

  4. facilitate field simulations, whether governed by differential equations, spectral models or based on computational models (ABM)

  5. construct a bridge between spatial data models and tensor data structures to facilitate the utilization of the latest artificial intelligence models

Contents:

  • Mesh to Field: Rasterization

    • Point Cloud Regularization

    • Line Network Voxelation

    • Mesh Surface Voxelation

    • Signed Distance Field

  • Field to Mesh: Isosurface

    • Boolean Marching Cubes

    • Marching Cubes

    • Surface Nets

  • Local Computation

    • Stencil / Kernels

      • von Neumann neighbourhood

      • Moore neighbourhood

      • Cube neighbourhood

      • Custom neighbourhoods

    • Universal Functions & Mathematical Operators (Numerical)

  • Field Simulations (Vectorized)

    • Dynamic Systems (based on Differential Equations)

    • Agent-Based Modeling

    • Cellular Automata

See below what was achieved with the old versions of Genesis Tools in our course Spatial Computing in Architectural Design, stay tuned for the results of Spatial Computing 3.0.