id 
ecaade2018_286 
authors 
Swahn, Erik 
year 
2018 
title 
Markovian Drift  Iterative substitutional synthesis of 2D and 3D design data using Markov models of source data 
source 
KepczynskaWalczak, A, Bialkowski, S (eds.), Computing for a better tomorrow  Proceedings of the 36th eCAADe Conference  Volume 2, Lodz University of Technology, Lodz, Poland, 1921 September 2018, pp. 113120 
summary 
This paper describes a general method for synthesizing discrete 2D and 3D output by building probabilistic models of rasterized or voxelized training data, and subsequently synthesizing new data iteratively by substituting cells or groups of cells in accordance with a learned transition matrix. The process is nondeterministic, stochastic and unsupervised. The size of the source data and output is arbitrary, and the source and output data can have an arbitrary set of cell states. Possible variations of the process are discussed, as well as possible applications in design processes on multiple scales. 
keywords 
Generative design; formal analysis; probabilistic models; Markov random fields; voxels; morphology 
series 
eCAADe 
email 
erik.swahn@gmail.com 
full text 
file.pdf (8,600,723 bytes) 
references 
Contenttype: text/plain

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last changed 
2018/07/24 10:23 
