面向增材制造材料的可扩展深度学习-加速拓扑优化
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
摘要：
拓扑优化（TO）是一种流行的和强大的计算方法，用于设计新的结构，材料和设备。两个计算挑战限制了TO对各种工业应用的适用性。首先，TO问题通常涉及大量的设计变量，以保证足够的表达能力。其次，许多TO问题需要大量昂贵的物理模型模拟，并且这些模拟不能并行化。为了解决这些问题，我们提出了一个通用的可扩展的深度学习（DL）为基础的TO框架，称为SDL-TO，它利用高性能计算（HPC）的并行方案，以加速设计增材制造（AM）材料的TO过程。与现有的TO的DL研究不同，我们的框架通过学习迭代历史数据并同时训练给定设计与其梯度之间的映射来加速TO。通过利用多个CPU上的并行计算并结合多个GPU上的分布式DL训练来学习替代梯度。学习的TO梯度实现了快速在线更新方案，而不是基于物理模拟器或求解器的昂贵更新。采用局部采样策略，降低了设计空间固有的高维性，提高了SDL-TO框架的训练精度和可扩展性。该方法是通过基准的例子和AM材料的热传导设计证明。与基线方法相比，拟议的SDL-TO框架显示出具有竞争力的性能，但与标准TO实现相比，速度提高了约8.6倍，从而显着降低了计算成本。
英文摘要：
Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.
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