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:
offer basic mathematical functionalities on field data models
offer functionalities of computational topology on top of the field structures
facilitate the conversion between mesh-based data models and field data models.
facilitate field simulations, whether governed by differential equations, spectral models or based on computational models (ABM)
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.