Visualization, Adaptation, and Transformation of Procedural Grammars

Lienhard, Stefan

doi:10.5075/epfl-thesis-7627

Cited by 1 publication

(5 citation statements)

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“…However, the relations between shape grammars and set grammars often are unclear due to fuzzy terminology in the field (Lienhard, 2017). Many terms circulate and may be confusing as they treat very similar concepts.…”

Section: Low-level Representation Of Geometrymentioning

confidence: 99%

“…As a first alternative, one may attempt to not let domain experts define the grammar, but to generate the rules based on design artifacts they produced in the past. The problem of automatically "learning" or creating a grammar from a given dataset is referred to as inverse procedural modeling and is commonly assessed to be very complex (Puppe, 1990;Lienhard, 2017). Nevertheless, a modest amount of research has been classified by Lienhard (2017).…”

Section: Facilitated Development Of Rewriting Rulesmentioning

confidence: 99%

“…The problem of automatically "learning" or creating a grammar from a given dataset is referred to as inverse procedural modeling and is commonly assessed to be very complex (Puppe, 1990;Lienhard, 2017). Nevertheless, a modest amount of research has been classified by Lienhard (2017). One set of works attempts to adapt relatively generic template grammars to a given dataset, mostly in the context of facade parsing (Nishida et al, 2016).…”

Section: Facilitated Development Of Rewriting Rulesmentioning

confidence: 99%

“…To remedy the computational load and complexity of filtering the entire solution space, some remedies exist. Lienhard (2017) proposed clustering methods for building designs that make the solution space more comprehensible to a user. Campbell et al (2009) and Kumar et al (2014) propose to only generate a small set of possible solutions from a search tree.…”

Section: Enumerative Generationmentioning

confidence: 99%

“…The latter is desirable but very difficult as grammargenerated designs often do not share a common configuration of objects (van Diepen and Shea, 2019;Grzesiak-Kopeć et al, 2021). For hierarchical and well-structured representations, the works of Alber and Rudolph (2003), Talton et al (2012), andLienhard (2017) propose a possible solution: instead of processing the individual designs generated, it might be more promising to consider the rules the subject of a genetic optimization. Different variants are generated by different grammars in the first step.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

Graph Rewriting Techniques in Engineering Design

Kolbeck

Vilgertshofer

Abualdenien

et al. 2022

Front. Built Environ.

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Capturing human knowledge underlying the design and engineering of products has been among the main goals of computational engineering since its very beginning. Over the last decades, various approaches have been proposed to tackle this objective. Among the most promising approaches is the application of graph theory for representing product structures by defining nodes representing entities and edges representing relations among them. The concrete meaning of these structures ranges from geometry representations over hierarchical product breakdowns to functional descriptions and flows of information or resources. On top of these graph structures, graph rewriting techniques provide another powerful layer of technology. By enabling the formal definition of rules for transforming graph structures, they allow on the one hand side to formally capture the engineering development process. On the other hand, the assembly of rewriting rules into graph grammars allows for an exhaustive search of the solution space of the engineering problem at hand. In combination with search strategies, an automated optimization of the design under given constraints and objectives can be realized. The paper provides an overview of the current state-of-the-art in graph rewriting and its applications in engineering design, with a focus on the built environment. It concludes with a discussion of the progress achieved and the missing research gaps.

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Section: Low-level Representation Of Geometrymentioning

confidence: 99%

Section: Facilitated Development Of Rewriting Rulesmentioning

confidence: 99%