jaxsso 0.0.1

A framework for structural shape optimization based on automatic differentiation (AD) and the adjoint method, enabled by JAX

JaxSSO

A framework for structural shape optimization based on automatic differentiation (AD) and the adjoint method, enabled by JAX.

Developed by Gaoyuan Wu @ Princeton.

Please share our project with others and cite us if you find it interesting and helpful. We have a preprint under review where you can find details regarding this framework. Cite us using:

Available soon

Features

• 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.

Background: shape optimization

We consider the minimization of the strain energy by changing the shape of structures, which is equivalent to maximizing the stiffness and reducing the bending in the structure. The mathematical formulation of this problem is as follows, where no additional constraints are considered. $$\text{minimize} \quad C(\mathbf{x}) = \frac{1}{2}\int\sigma\epsilon \mathrm{d}V = \frac{1}{2}\mathbf{f}^\mathrm{T}\mathbf{u}(\mathbf{x}) $$ $$\text{subject to: } \quad \mathbf{K}(\mathbf{x})\mathbf{u}(\mathbf{x}) =\mathbf{f}$$ where $C$ is the compliance, which is equal to the work done by the external load; $\mathbf{x} \in \mathbb{R}^{n_d}$ is a vector of $n_d$ design variables that determine the shape of the structure; $\sigma$, $\epsilon$ and $V$ are the stress, strain and volume, respectively; $\mathbf{f} \in \mathbb{R}^n$ and $\mathbf{u}(\mathbf{x}) \in \mathbb{R}^n$ are the generalized load vector and nodal displacement of $n$ structural nodes; $\mathbf{K} \in \mathbb{R}^{6n\times6n}$ is the stiffness matrix. The constraint is essentially the governing equation in finite element analysis (FEA).

To implement gradient-based optimization, one needs to calculate $\nabla C$. By applying the adjoint method, the entry of $\nabla C$ is as follows: $$\frac{\partial C}{\partial x_i}=-\frac{1}{2}\mathbf{u}^\mathrm{T}\frac{\partial \mathbf{K}}{\partial x_i}\mathbf{u}$$ The use of the adjoint method: i) reduces the computation complexity and ii) decouples FEA and the derivative calculation of the stiffness matrix $\mathbf K$. To get $\nabla C$:

1. Conduct FEA to get $\mathbf u$
2. Conduct sensitivity analysis to get $\frac{\partial \mathbf{K}}{\partial x_i}$. which is done by the module in JaxSSO called Model_Sens.py using AD.

Usage

Installation

Install it with pip: pip install JaxSSO

Dependencies

JaxSSO is written in Python and requires:

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

Optional:

• PyNite: A 3D structural engineering finite element library for Python, which is used herein for structural analysis. You choose any FEA solver you'd prefer (e.g. opensees) and couple it with JaxSSO.

Quickstart

The project provides you with interactive examples with Google Colab for quick start:

• 2D-arch: form-finding of a 2d-arch
• 3D-arch: form-finding of a 3d-arch
• Mannheim Multihalle: form-finding of Mannheim Multihalle
• Four-point supported gridshell: form-finding of a gridshell with four coner nodes pinned. The geometry is parameterized by Bezier Surface.
• Two-edge supported canopy, unconstrained: form-finding of a canopy. The geometry is parameterized by Bezier Surface.
• Two-edge supported canopy, constrained: form-finding of a canopy with height constraints. The geometry is parameterized by Bezier Surface.