Adjoints of large simulation codes through Automatic Differentiation
Adjoints of large simulation codes through Automatic Differentiation
复制标题
通过自动微分实现大型模拟代码的伴随
DOI：
10.3166/remn.17.63-86
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发表时间：
2008
影响因子：
1.2
通讯作者：
Benjamin Dauvergne
中科院分区：
文献类型：
--
作者：
L. Hascoët;Benjamin Dauvergne
伴随方法是获取大型仿真代码梯度的首选方法。自动微分已经为几个模拟代码产生了伴随代码，研究继续将其应用于更大的应用。我们比较了现有自动微分工具所选择的方法来构建伴随算法。这些方法共享与数据流和内存流量相关的类似问题。我们给出了这些问题的一些最新的答案，并给出了一些应用的结果。
Adjoint methods are the choice approach to obtain gradients of large simulation codes. Automatic Differentiation has already produced adjoint codes for several simulation codes, and research continues to apply it to even larger applications. We compare the approaches chosen by existing Automatic Differentiation tools to build adjoint algorithms. These approaches share similar problems related to data-flow and memory traffic. We present some current state-of-the-art answers to these problems, and show the results on some applications.