# Topology optimization based on moving deformable components: A new computational framework

arXiv: https://arxiv.org/abs/1404.4820 · HTML: https://mlsanity.com/paper/1404.4820/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2014.

## Built on by (most cited first, 6 of 6)

- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [Neural topology optimization: the good, the bad, and the ugly](https://mlsanity.com/paper/2407.13954/) (2024)
- [XVoxel-Based Parametric Design Optimization of Feature Models](https://mlsanity.com/paper/2303.15316/) (2023)
- [Structural Design Through Reinforcement Learning](https://mlsanity.com/paper/2407.07288/) (2024)
- [Data-driven topology design based on principal component analysis for 3D structural design problems](https://mlsanity.com/paper/2409.01607/) (2024)
- [Enhanced Data-driven Topology Design Methodology with Multi-level Mesh and Correlation-based Mutation for Stress-related Multi-objective Optimization](https://mlsanity.com/paper/2504.14790/) (2025)

## Abstract (arXiv)

In the present work, a new computational framework for structural topology optimization based on the concept of moving deformable components is proposed. Compared with the traditional pixel or node point-based solution framework, the proposed solution paradigm can incorporate more geometry and mechanical information into topology optimization directly and therefore render the solution process more flexible. It also has the great potential to reduce the computational burden associated with topology optimization substantially. Some representative examples are presented to illustrate the effectiveness of the proposed approach.