cashocs: A Computational, Adjoint-Based Shape Optimization and Optimal Control Software

TL;DR

cashocs is a Python software package that simplifies PDE-constrained optimization problems by automating adjoint calculations and shape derivatives, leveraging FEniCS for easy problem definition and solution.

Contribution

The paper introduces cashocs, a novel Python tool that automates adjoint-based shape optimization and optimal control for PDEs, integrating with FEniCS for user-friendly application.

Findings

Demonstrates ease of use and applicability of cashocs.

Automates derivation of adjoint systems and derivatives.

Facilitates PDE-constrained optimization in scientific and industrial contexts.

Abstract

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.