MOTO: Topology Optimization for Large Deformations via an Implicit Material Point Method
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914395705049088 |
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| author | Padhy, Rahul Kumar Chandrasekhar, Aaditya Suresh, Krishnan |
| author_facet | Padhy, Rahul Kumar Chandrasekhar, Aaditya Suresh, Krishnan |
| contents | The Finite element method (FEM) has long served as the computational backbone for topology optimization (TO). However, for designing structures undergoing large deformations, conventional FEM-based TO often exhibits numerical instabilities due to severe mesh distortions, tangling, and large rotations, consequently leading to convergence failures.
To address this challenge, we present a TO framework based on the Material Point Method (MPM). MPM is a hybrid Lagrangian-Eulerian particle method, well-suited for simulating large deformations. In particular, we present an end-to-end differentiable implicit MPM framework for designing structures undergoing quasi-static hyperelastic large deformations. The effectiveness of the approach is demonstrated through validation studies encompassing both single and multi-material designs, including the design of compliant soft robotic grippers. The software accompanying this paper can be accessed at github.com/UW-ERSL/MOTO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14596 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MOTO: Topology Optimization for Large Deformations via an Implicit Material Point Method Padhy, Rahul Kumar Chandrasekhar, Aaditya Suresh, Krishnan Computational Engineering, Finance, and Science The Finite element method (FEM) has long served as the computational backbone for topology optimization (TO). However, for designing structures undergoing large deformations, conventional FEM-based TO often exhibits numerical instabilities due to severe mesh distortions, tangling, and large rotations, consequently leading to convergence failures. To address this challenge, we present a TO framework based on the Material Point Method (MPM). MPM is a hybrid Lagrangian-Eulerian particle method, well-suited for simulating large deformations. In particular, we present an end-to-end differentiable implicit MPM framework for designing structures undergoing quasi-static hyperelastic large deformations. The effectiveness of the approach is demonstrated through validation studies encompassing both single and multi-material designs, including the design of compliant soft robotic grippers. The software accompanying this paper can be accessed at github.com/UW-ERSL/MOTO. |
| title | MOTO: Topology Optimization for Large Deformations via an Implicit Material Point Method |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2603.14596 |