AuTO: a framework for Automatic differentiation in Topology Optimization
AuTO: a framework for Automatic differentiation in Topology Optimization
复制标题
DOI：
10.1007/s00158-021-03025-8
复制
发表时间：
2021-04
影响因子：
3.9
通讯作者：
A. Chandrasekhar;S. Sridhara;K. Suresh
中科院分区：
文献类型：
--
作者：
A. Chandrasekhar;S. Sridhara;K. Suresh
拓扑优化（TO）的关键步骤是寻找灵敏度。手工推导和实现灵敏度是相当费力和容易出错的，特别是对于重要的目标、约束和材料模型。另一种方法是利用自动区分（AD）。虽然AD已经存在了几十年，并且也应用于TO，但它的广泛采用在很大程度上是缺席的。在这篇教育论文中，我们的目标是重新为to引入AD，通过说明性代码使其易于访问。特别地，我们使用了JAX，这是一个高性能的Python库，用于自动计算从用户定义到问题的灵敏度。由此产生的框架，在这里称为AuTO，通过几个例子来说明合规最小化，合规机构设计和微观结构设计。
A critical step in topology optimization (TO) is finding sensitivities. Manual derivation and implementation of sensitivities can be quite laborious and error-prone, especially for non-trivial objectives, constraints and material models. An alternate approach is to utilize automatic differentiation (AD). While AD has been around for decades, and has also been applied in TO, its wider adoption has largely been absent. In this educational paper, we aim to reintroduce AD for TO, making it easily accessible through illustrative codes. In particular, we employ JAX, a high-performance Python library forautomatically computing sensitivities from a user-definedTOproblem. The resulting framework, referred to here as AuTO, is illustrated through several examples in compliance minimization, compliant mechanism design and microstructural design.