2021
DOI: 10.48550/arxiv.2103.02588
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IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures

Jun Wang,
Wei Wayne Chen,
Daicong Da
et al.

Abstract: Variable-density cellular structures can overcome connectivity and manufacturability issues of topologicallyoptimized, functionally graded structures, particularly when those structures are represented as discrete density maps. One naïve approach to creating variable-density cellular structures is simply replacing the discrete density map with an unselective type of unit cells having corresponding densities. However, doing so breaks the desired mechanical behavior, as equivalent density alone does not guarante… Show more

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Cited by 2 publications
(6 citation statements)
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“…On the other hand, the corresponding property distributions in Figure 2(c) show considerable imbalance, which epitomizes that data balance in parametric shape space does not ensure that in property space. By extension, we argue that such property imbalance is prevalent in many metamaterial datasets generated by space-filling design in parametric shape space [16,11,17,22,23]. We claim that: (i) any metamaterial dataset collected based on naive sampling in parametric shape space are subject to substantial property bias [16,11,17,22,23], and more importantly, (ii) this is highly likely to hold true for datasets with generic design representations -other than parametric ones -as well [11,19,10,24,25].…”
Section: Property Bias Induced By Nearly-uniform Sampling In Shape Spacementioning
confidence: 90%
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“…On the other hand, the corresponding property distributions in Figure 2(c) show considerable imbalance, which epitomizes that data balance in parametric shape space does not ensure that in property space. By extension, we argue that such property imbalance is prevalent in many metamaterial datasets generated by space-filling design in parametric shape space [16,11,17,22,23]. We claim that: (i) any metamaterial dataset collected based on naive sampling in parametric shape space are subject to substantial property bias [16,11,17,22,23], and more importantly, (ii) this is highly likely to hold true for datasets with generic design representations -other than parametric ones -as well [11,19,10,24,25].…”
Section: Property Bias Induced By Nearly-uniform Sampling In Shape Spacementioning
confidence: 90%
“…Underestimating the risk, common practice in DDMD typically resorts to a large number of space-filling designs in the shape space spanned by the shape parameters. This inevitably hosts imbalance -distributional bias of data -in the property space [15,16,11,17] formed by the property vectors. The succeeding tasks involving a data-driven modeltraining, validation, and deployment to design -follow mostly without rigorous assessment on data quality in terms of diversity, design quality, feasibility, etc.…”
Section: Introductionmentioning
confidence: 99%
“…While we validated our proposed model on a 3D shape synthesis example, the method is not restricted to this application. For example, by replacing the latent vector with parameters of unit cell shapes, this model can also help address the inverse design problem of cellular structures [24].…”
Section: Discussionmentioning
confidence: 99%
“…This allows us to generate many designs conditioned on any performance requirement; i.e., the mapping from the performance space to the design space can be one-to-many. This technique has been used for the inverse design of metasurfaces, metamaterials, and cellular structures [12,13,24]. As pointed out in [25], however, conditional generative models with continuous conditions may fail.…”
Section: Inverse Design Problemmentioning
confidence: 99%
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