Inductive Machine Learning in Microstructures

Hanna, Sean; Mahdavi, Siavash Haroun

doi:10.1007/978-1-4020-5131-9_29

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…From this perspective an interesting approach would be the coupling of the GA with a supervised machine-learning algorithm such as the artificial neural network (ANN) or the support vector machine (SVM). In this case the GA would be used to evolve either shading volumes or alternative topologies, while the SVM, once trained with a dataset gathered from the simulation tool, would output the optimized solutions (Hanna and Mahdavi 2006). Similarly, other studies have proved the efficacy of combining a GA with an ANN in order to improve the processing time of the optimization (Wong et al 2010).…”

Section: Figure 11mentioning

confidence: 99%

Performative Topologies: An Evolutionary Shape Optimization Framework for Daylighting Performance Coupling a Particle-Spring System With an Energy Simulation Tool

Castorina¹

2012

Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA)

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Section: Figure 11mentioning

confidence: 99%

Performative Topologies: An Evolutionary Shape Optimization Framework for Daylighting Performance Coupling a Particle-Spring System With an Energy Simulation Tool

Castorina¹

2012

Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA)

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

Hanna

2007

AIEDAM

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While structural optimisation is usually handled by iterative methods requiring repeated samples of a physics-based model, this process can be computationally demanding. Given a set of previously optimised structures of the same topology, this paper uses inductive learning to replace this optimisation process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimisation, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimisations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimisation very closely after sufficient training, and has also been found effective at generalising the underlying optima to produce structures that perform better than those found by standard iterative methods.

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Inductive Machine Learning in Microstructures

Cited by 2 publications

References 16 publications

Performative Topologies: An Evolutionary Shape Optimization Framework for Daylighting Performance Coupling a Particle-Spring System With an Energy Simulation Tool

Performative Topologies: An Evolutionary Shape Optimization Framework for Daylighting Performance Coupling a Particle-Spring System With an Energy Simulation Tool

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

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