Forking paths

Here, each plan is a single, continuous forking space generated using a depth-first search algorithm until all cells are visited and all walls are removed in the path of the search – then sorted by wall layout.

The number of cells in the space limits the number of unique alternatives generatable (removing mirrored and rotated duplicates).

Skärmbild (204)

2 x 2 cells 1 unique maze
2 x 3 cells 5 unique mazes
2 x 4 cells 12 unique mazes
2 x 5 cells 31 unique mazes

3 x 3 cells 12 unique mazes
3 x 4 cells 112 unique mazes
3 x 5 cells 509 unique mazes
3 x 6 cells 2133 unique mazes

4 x 4 cells 481 unique mazes
4 x 5 cells 5395 unique mazes
4 x 6 cells 20132 unique mazes

5 x 5 cells 9054 unique mazes

Here, two openings are created on side walls, and the size of cell columns and rows are randomly expanded in 1:1 / 2:1 proportions.

From bits to matter, from plan to space

An alphabet: laser cutter operations

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Plan generation with nesting shape grammars in the Forsythia editor, written by John Greene:

https://github.com/johnalexandergreene/Forsythia/tree/master/app/grammarEditor

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Going from plans to laser cut sheet:

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From plans to cavities and elevations:slutpres10.pngslutpres9.png

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From grammar to building:slutpres14.png

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Stochastic Reconstruction

Iterative substitutions in two- and three-dimensional matrices
using Markov random field models of von Neumann neighborhoods

Tiling

 

 

Building a probabilistic model

Skärmklipp 2018-01-29 16.01.17.png

Iterative substitutions in two dimensions

Skärmklipp 2018-01-29 15.59.36.png

Iterative substitutions in three dimensions, starting with source data

Skärmklipp 2018-01-29 16.01.28.png

Iterative substitutions in three dimensions, starting with Perlin noise

Skärmklipp 2018-01-29 16.01.44Skärmklipp 2018-01-29 16.01.41

Isolated bodies

Skärmklipp 2018-01-29 16.01.50.png

Machine Learning for Artists

In my current project, I’m working on generative processes with some connections to machine learning techniques. I just found a great resource, Machine Learning for Artists, with lots of video lectures and code related to principle component analysis, neural networks, deep learning and much more.

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http://ml4a.github.io/index/

Check out the “classes” tab for the video lectures.

Digital Grotesque

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For anyone interested in the techniques behind the Digital Grotesque project and others by Michael Hansmeyer / Benjamin Dillenburger, check out these course pages from ETH Zürich with Processing scripts:

http://www.caad.arch.ethz.ch/blog/high-resolution-2-materialise/

http://www.caad.arch.ethz.ch/blog/high-resolution-architecture-sacred-spaces/

More of the theory behind it is described in the article “Mesh Grammars –
Procedural Articulation of Form”:

https://cumincad.architexturez.net/system/files/pdf/caadria2013_259.content.pdf