论文标题
CASHOC：一种基于计算的基于伴随的形状优化和最佳控制软件
cashocs: A Computational, Adjoint-Based Shape Optimization and Optimal Control Software
论文作者
论文摘要
受部分微分方程（PDE）限制的优化问题解决方案在许多科学和工业领域都起着重要作用。在这项工作中，我们提出了Cashocs，这是一个用Python编写的新软件包，该软件包在最佳控制和形状优化的背景下自动解决了此类问题。 Software Cashocs实施了连续伴随方法的离散化，该方法以自动化方式得出了必要的伴随系统和（形状）衍生物。由于Cashocs基于有限元软件Fenics，因此它继承了其简单的高级用户界面。这使我们可以直接定义和解决PDE在我们的软件中限制了优化问题。在本文中，我们讨论了Cashocs的设计和功能，还展示了其直接的可用性和适用性。
The solution of optimization problems constrained by partial differential equations (PDEs) plays an important role in many areas of science and industry. In this work we present cashocs, a new software package written in Python, which automatically solves such problems in the context of optimal control and shape optimization. The software cashocs implements a discretization of the continuous adjoint approach, which derives the necessary adjoint systems and (shape) derivatives in an automated fashion. As cashocs is based on the finite element software FEniCS, it inherits its simple, high-level user interface. This makes it straightforward to define and solve PDE constrained optimization problems with our software. In this paper, we discuss the design and functionalities of cashocs and also demonstrate its straightforward usability and applicability.
