Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines

Hanna, Sean

doi:10.1017/s0890060407000327

Cited by 19 publications

(15 citation statements)

References 22 publications

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“…In more recent years a number of authors have proposed using machine learning techniques to assist human designers. In general these are for domain specific applications, such as for architectural space frame structures (Hanna 2007), structurally valid furniture (Umetani et al 2012) or aircraft designs (Oberhauser et al 2015). In these systems machine learning is typically used to learn about specific properties of the system.…”

Section: Evolutionary Methods and Machine Learningmentioning

confidence: 99%

On Hybrid Creativity

Lomas

2018

Arts

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This article reviews the development of the author's computational art practice, where the computer is used both as a device that provides the medium for generation of art ('computer as art') as well as acting actively as an assistant in the process of creating art ('computer as artist's assistant'), helping explore the space of possibilities afforded by generative systems. Drawing analogies with Kasparov's Advanced Chess and the deliberate development of unstable aircraft using fly-by-wire technology, the article argues for a collaborative relationship with the computer that can free the artist to more fearlessly engage with the challenges of working with emergent systems that exhibit complex unpredictable behavior. The article also describes 'Species Explorer', the system the author has created in response to these challenges to assist exploration of the possibilities afforded by parametrically driven generative systems. This system provides a framework to allow the user to use a number of different techniques to explore new parameter combinations, including genetic algorithms, and machine learning methods. As the system learns the artist's preferences the relationship with the computer can be considered to change from one of assistance to collaboration.

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Section: Evolutionary Methods and Machine Learningmentioning

confidence: 99%

On Hybrid Creativity

Lomas

2018

Arts

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“…In more recent years a number of authors have proposed using machine learning techniques to assist human designers. In general these are for domain specific applications, such as for architectural space frame structures (Hanna 2007), structurally valid furniture (Umetani, Igarashi & Mitra 2012) or aircraft designs (Oberhauser, et al 2015). In these systems machine learning is typically used to learn about specific properties of the system.…”

Section: Figure 1: Chart Exploring the Effect Of Varying Parameter Vamentioning

confidence: 99%

Species Explorer: An interface for artistic exploration of multi-dimensional parameter spaces

Lomas

2016

Electronic Workshops in Computing

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This paper describes Species Explorer, an interface to allow creative exploration of generative systems with multi-dimensional parameter spaces. The system combines both evolutionary and machine learning approaches. It was originally designed to assist creating work for the author's 'Cellular Forms' and 'Hybrid Forms' series, where a large number of parameters are used to yield emergent results, but is a general framework that could be applied to many other systems.Generative art. Evolutionary design. Machine learning. Computationally assisted design.

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“…Aiming to avoid local optima, the mutation function had a 5% randomness. 20 The system used in the study produced 1000 simulations as the training set and 400 as the testing set. Figure 9 shows the flow chart of the process.…”

Section: The Digital Optimization Process (Ga)mentioning

confidence: 99%

Adaptive behavior of structural systems in unpredictable changing environments by using self-learning algorithms: A case study

2014

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Adaptive architecture is expected to improve the performance of buildings and create more efficient building systems. One of the major research areas under this scope is the adaptive behavior of structural elements affected by load distribution. In order to achieve this, current studies develop structures that adapt by either following a database of precalculated equilibrium solutions or using self-learning algorithms to acquire active control systems to structures. This paper examined a case study element, which demonstrates an adaptive behavior in real time, based on self-learning abilities. The focus of this experiment was to gain control over a structural system as a whole (not only on a singular component) according to both objective and subjective parameters, that is, both load distribution parameters and spatial parameters, which are design related. The examined structural element was a canopy, situated in a dynamic environment that brought a change in the element's load distribution. The learning ability was given by applying a supervised learning algorithm-Artificial Neural Network (ANN)-on a physical prototype. The ANN was trained by an optimized database of finite solutions, which was created by a Genetic Algorithm. Through this method, complex calculations are conducted ''offline'', and the component operates in a ''decision-making'' mode in real time, adapting to a versatile environment while using minimal computational resources. Results show that the case study successfully exhibited self-learning and acquired the ability to adapt to unpredictable changing forces while keeping certain design requirements. This method can be applied over different structural elements (fac xade elements, canopies, structural components, etc.) to achieve adaptation to various parameters with an unpredictable pattern, such as human behavior or weather conditions.

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Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines

Cited by 19 publications

References 22 publications

On Hybrid Creativity

On Hybrid Creativity

Species Explorer: An interface for artistic exploration of multi-dimensional parameter spaces

Adaptive behavior of structural systems in unpredictable changing environments by using self-learning algorithms: A case study

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