Fast inverse design of microstructures via generative invariance networks
Fast inverse design of microstructures via generative invariance networks
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DOI：
10.1038/s43588-021-00045-8
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发表时间：
2021-03-01
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影响因子：
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通讯作者：
Sarkar, Soumik
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文献类型：
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作者：
Lee, Xian Yeow;Waite, Joshua R.;Sarkar, Soumik
有效设计具有所需性能的材料微观结构的问题涉及各种工程和科学应用。快速生成具有用户指定属性分布的微结构的能力可以改变传统微结构敏感设计的迭代过程。我们使用约束生成对抗网络（GAN）模型重新制定微观结构设计过程。这种方法明确地编码 GAN 内的不变性约束，以生成符合设计规范的光伏应用的两相形态：特别是用户定义的短路电流密度和填充因子组合。这种不变性约束可以通过将微观结构映射到光伏特性的完整物理模型的可微的、基于深度学习的替代物来表示。此外，我们提出了一种多保真度替代方案，可以将昂贵的标签要求降低五倍。我们的框架能够结合昂贵的或不可微的约束，以快速生成具有用户定义属性的微结构（190 毫秒内）。这种针对逆向设计问题提出的物理感知数据驱动方法可用于显着加速微观结构敏感设计领域的发展。
The problem of the efficient design of material microstructures exhibiting desired properties spans a variety of engineering and science applications. The ability to rapidly generate microstructures that exhibit user-specified property distributions can transform the iterative process of traditional microstructure-sensitive design. We reformulate the microstructure design process using a constrained generative adversarial network (GAN) model. This approach explicitly encodes invariance constraints within GANs to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, user-defined short-circuit current density and fill factor combinations. Such invariance constraints can be represented by differentiable, deep learning-based surrogates of full physics models mapping microstructures to photovoltaic properties. Furthermore, we propose a multi-fidelity surrogate that reduces expensive label requirements by a factor of five. Our framework enables the incorporation of expensive or non-differentiable constraints for the fast generation of microstructures (in 190 ms) with user-defined properties. Such proposed physics-aware data-driven methods for inverse design problems can be used to considerably accelerate the field of microstructure-sensitive design.