MorphOpt —— A Differentiable Optimization Framework for Soft Structure & Morphology Design

Developed by Zenan Song

A differentiable structural optimization framework that eliminates manual sensitivity derivation by fusing the adjoint method with automatic differentiation (AD). The sensitivity corresponds to the virtual work of residual force derivatives on the adjoint displacement field, computed via a single backpropagation through the residual graph. MorphOpt provides a unified interface for geometry, load, and material definition, calls torchfea for GPU-accelerated differentiable nonlinear FEA and adjoint-AD sensitivity, and employs a trust-region optimizer with L-BFGS for design updates. Validated on SIMP topology optimization with B-spline density fields and pneumatic soft robot shape optimization with deformation-dependent follower loads.

Features

• Custom model definition covering geometry, material, and load parameters
• Differentiable FEA via torchfea with automatic solving and sensitivity propagation
• L-BFGS and trust-region methods for parameter updates within a subproblem framework
• Fully extensible via inheritance — users customize every component in their definition scripts

Quick Start

pip install morphopt

1. Install dependencies. Python ≥ 3.12, PyTorch (float64) recommended.
2. Run a minimal example:

# Shape optimization
python examples/basic/shapeoptimization.py

# SIMP topology optimization
python examples/basic/simp.py

3. More usage examples:

• Task definition guide: docs/module_definition_guide.md
• Module API reference: docs/module_reference.md
• Theory & papers: docs/theory/

Directory Overview

• src/morphopt/
  • optcore — Core package (controller, params, solver, objective, updaters)
  • shapeopt/ — B-spline shape optimization
  • simp/ — SIMP topology optimization
• examples/ — Example tasks (bending actuator, gripper, etc.)
• docs/ — Usage guides, API reference, and theory papers
• tests/ — Gradient checks, geometry tests, UI tests

License

Research use preferred. For production or commercial use, please evaluate and ensure numerical and engineering robustness first.

Citation

If you use MorphOpt in your research, please cite:

• TRO 2023 — Morphology Design
• TRO 2026 — Jacobian-based Optimization
• TMECH 2026 — Contact-aware Design