Peter Von Buelow

Dr. Peter Von Buelow

Architecture, University of Michigan, Ann Arbor, Michigan, USA
Professor

Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 October 2011 in Advanced Engineering Informatics
Verified
Journal Article
Closed access
MTMichela Turrin
Michela Turrin
Peter Von BuelowPeter Von Buelow
Peter Von Buelow
Rudi StouffsRudi Stouffs
Rudi Stouffs

5 March 2008 in Digital Creativity
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Journal Article
Closed access
Peter Von BuelowPeter Von Buelow
Peter Von Buelow
Finding creative solutions to design problems depends heavily on a fruitful exploration in early phases. Many aspects of evolutionary computation (EC) and in particular genetic algorithms (GA) make them highly suited as computational tools for discovering good solutions. This paper discusses specific aspects of the GA method which parallel traditional design methodologies described by creativity researchers including Gordon, deBono, Parnes, and Osborn among others. Because EC methods work with populations of ‘fairly good’ solutions, there is less danger that creativity will be harmed by design fixation, on one ‘best’ solution. An example application is demonstrated using the design of a small truss bridge. The solutions offered by the application are varied enough to allow the designer a choice of forms. At the same time, all of the solutions offered are ‘fairly good’. This demonstrates the aspects of EC which make it well suited for creative exploration of problems.


1 June 2011 in International Journal of Architectural Computing
Verified
Journal Article
Closed access
MAMaria Vera van Embden Andres
Maria Vera van Embden Andres
Michela TurrinMichela Turrin
Michela Turrin
Peter Von BuelowPeter Von Buelow
Peter Von Buelow
The approach demonstrated in this paper uses Evolutionary Computation (EC) to enhance and modify structural form based on biological micro structures. The forms are modified to conform to new boundary conditions associated with architectural structures. The process is based on a Genetic Algorithm (GA) which visually exposes for the designer a range of good performing solutions within the design space. The application of the GA is combined with parametric software, in this case Generative Components (GC). The program described here as ParaGen (Parametric Genetic Algorithm), uses a Finite Element Analysis (FEA) to determine the structural performance of the forms. This allows the designer to manipulate and optimize a parametrically defined model based on predefined criteria and parameters. The opportunities and limitations of this design process are explored and evaluated based on an experimental case study using topologies based on radiolarian skeletons. The design procedure described includes user interaction in the exploration of solutions that perform well both for the explicitly defined programmatic criteria (structural) as well as for the implicit criteria provided by the designer (visual aesthetic).