Generating optimal topologies in structural design using a homogenization method
Computer Methods in Applied Mechanics and Engineering
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Using evolutionary algorithms to aid designers of architectural structures
Creative evolutionary systems
The "What" and "How" of Learning in Design
IEEE Expert: Intelligent Systems and Their Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to set up numerical optimizations of engineering designs
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Parameterized versus generative representations in structural design: an empirical comparison
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Beyond simulation: designing for uncertainty and robust solutions
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Procedural function-based modelling of volumetric microstructures
Graphical Models
Proceedings of the Symposium on Simulation for Architecture & Urban Design
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Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization 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 optimization, 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 optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods.