A differentiable finite element analysis (FEA) solver for structural optimization, enabled by JAX.

Developed by Gaoyuan Wu @ Princeton.

• Automatic differentiation (AD): an easy and accurate way for gradient evaluation. The implementation of AD avoids deriving derivatives manually or trauncation errors from numerical differentiation.
• Acclerated linear algebra (XLA) and just-in-time compilation: these features in JAX boost the gradient evaluation
• Hardware acceleration: run on GPUs and TPUs for faster experience
• Support beam-column elements and MITC-4 quadrilateral shell elements
• Shape optimization, size optimization and topology optimization
• Seamless integration with machine learning (ML) libraries

Install it with pip: pip install JaxSSO

JaxSSO is written in Python and requires:

• numpy >= 1.22.0.
• JAX: "JAX is Autograd and XLA, brought together for high-performance machine learning research." Please refer to this link for the installation of JAX.
• scipy.

Optional:

• Nlopt: Nlopt is a library for nonlinear optimization. It has Python interface, which is implemented herein. Refer to this link for the installation of Nlopt. Alternatively, you can use pip install nlopt, please refer to nlopt-python.
• Flax: neural network library based on JAX. JAXSSO can be integrated with flax, please see Examples/Neural_Network_Topo_Shape.ipynb
• Optax: optimization library based on JAX, can be used to train neural networks.

The project provides you with interactive examples with Google Colab for quick start. No installation locally is required.

• Shape optimization of continuous shell

• Size (thickness) optimization of continuous shell

• Simultaneous shape & topology optimization

• Simultaneous shape & topology optimization-2

• Shape optimization of grid shell: geometry from Favilli et al. 2024

Please star, share our project with others and/or cite us if you find our work interesting and helpful.

We have a new manuscript under review.

Our previous work can be seen in this paper. Cite our previous work using:

@article{wu_framework_2023,
	title = {A framework for structural shape optimization based on automatic differentiation, the adjoint method and accelerated linear algebra},
	volume = {66},
	issn = {1615-1488},
	url = {https://doi.org/10.1007/s00158-023-03601-0},
	doi = {10.1007/s00158-023-03601-0},
	language = {en},
	number = {7},
	urldate = {2023-06-21},
	journal = {Structural and Multidisciplinary Optimization},
	author = {Wu, Gaoyuan},
	month = jun,
	year = {2023},
	keywords = {Adjoint method, Automatic differentiation, Bézier surface, Form finding, JAX, Shape optimization, Shell structure},
	pages = {151},
}