Persistent homology-based explicit topological control for 2D topology optimization with MMA

Abstract

Controlling structural complexity, particularly the number of holes, remains a fundamental challenge in topology optimization, with significant implications for both theoretical analysis and manufacturability. Most existing approaches rely on indirect strategies, such as filtering techniques, minimum length-scale control, or specific level-set initializations, which influence topology only implicitly and do not allow precise regulation of topological features. In this work, we propose an explicit and differentiable topology-control framework by integrating persistent homology into the classical minimum-compliance topology optimization problem. The design domain and density field are represented using non-uniform rational B-splines (NURBS), while persistence diagrams are employed to rigorously and quantitatively characterize topological features. Given a prescribed number of holes, a differentiable topology-aware objective is constructed from the persistence pairs and incorporated into the compliance objective, leading to a unified optimization formulation. The resulting problem is efficiently solved using the method of moving asymptotes (MMA).Numerical experiments demonstrate that the proposed approach enables explicit control over structural connectivity and the number of holes, thereby providing a systematic and mathematically grounded strategy for topology regulation.