Topology Optimization

In this repository we explore the utilization of deep neural networks to power structure and topological optimization.

Installation

conda create -n ncvx_exp_pami python=3.9

Install pygranso

git clone https://github.com/sun-umn/PyGRANSO.git

cd PyGRANSO

pip install git+https://github.com/sun-umn/PyGRANSO@dimension_factor

pip install -r requirements.txt -f https://download.pytorch.org/whl/cu111/torch_stable.html

(Optional, not used in this src) Install neural-structral-optimization

pip install -q tf-nightly git+https://github.com/google-research/neural-structural-optimization.git

Problem Description

The PyGRANSO implementation is based on the MBB beam example of neural-structral-optimization. See section MBB Beam (Figure 2 from paper) of https://github.com/google-research/neural-structural-optimization/blob/master/notebooks/optimization-examples.ipynb for more details.

Running the code

tasks.py is the main code module to run code. Our current results are the outputs of the function run_multi_structure_pipeline. Assuming, everything is setup correctly our results can be reproduced via the following code blocks:

from tasks import run_multi_structure_pipeline

run_multi_structure_pipeline()

or with an MSI job:

sbatch jobs/multi_structure_outputs.slurm