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Microstructure design using a human computation game
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AI
DOI：10.1016/j.mtla.2022.101544.png)
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Abstract
En 中文
The design of microstructures that are optimized for a given engineering applications requires exploration of a rough and high-dimensional configuration space. Gradient-based algorithms are efficient, but suffer from a propensity to get stuck in local minima. Global-optimization algorithms are better at finding global minima, but are generally slow to converge. We developed and tested a Human Computation Game (HCG) for microstructure design where players interactively manipulate the microstructure to optimize an effective macroscopic material property. We investigate the impact of various game mechanics on solution quality and efficiency, and compare the HCG player solutions to those of a traditional global optimization algorithm-Simulated Annealing (SA). We show that organizing players into Synchronous teams performed better on more complex problems on average than players working Asynchronously or Solo. We also show that in the best cases, players can find microstructures that outperform those obtained by SA by up to 25% using the same number of computations, or achieve the same performance using up to 307 times fewer computational steps. By studying the optimization strategies employed by HCG players, we anticipate that improved optimization algorithms for microstructure design (and other configurational optimization problems) can be developed.
Keywords:
Grain boundary
Grain boundary networks
Human-computer interaction