Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Renumics/mesh2vec
Turn CAE mesh data => aggregated element feature vectors for ML
https://github.com/Renumics/mesh2vec
cae d3plot deep-learning dyna ls-dyna machine-learning python
Last synced: about 1 month ago
JSON representation
Turn CAE mesh data => aggregated element feature vectors for ML
- Host: GitHub
- URL: https://github.com/Renumics/mesh2vec
- Owner: Renumics
- License: mit
- Created: 2023-02-20T09:34:47.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-02-29T15:03:14.000Z (10 months ago)
- Last Synced: 2024-05-08T00:15:38.289Z (8 months ago)
- Topics: cae, d3plot, deep-learning, dyna, ls-dyna, machine-learning, python
- Language: Python
- Homepage:
- Size: 14.5 MB
- Stars: 12
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Mesh2Vec
========
Quickstart
-----------
mesh2vec
Turn CAE mesh data into aggregated element feature vectors for ML
Latest Documentation
## 🚀 Introduction
Mesh2vec is a tool that facilitates the import of Computer-Aided Engineering (CAE) mesh data from [LS-DYNA](https://www.ansys.com/de-de/products/structures/ansys-ls-dyna).
It utilizes various quality metrics of elements and their surrounding neighborhood to aggregate feature vectors for each element. These feature vectors are of equal length and can be effectively utilized as inputs for machine learning methods. This represents a simpler and more efficient alternative to traditional mesh and graph-based approaches for automatic mesh quality analysis.
## ⏱️ Quickstart
### Installation
1. Create and activate a [virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/).
2. Use the following command to install mesh2vec into your environment:
```bash
pip install mesh2vec
```
3. Please make sure you have an environment variable ANSA_EXECUTABLE set pointing to your ANSA executable to use ANSA depended features like shell and feature import.
4. You may temporarily need to set an environment variable SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True.
### Load Mesh
```python
from pathlib import Path
from mesh2vec.mesh2vec_cae import Mesh2VecCae
m2v = Mesh2VecCae.from_ansa_shell(4,
Path("data/hat/Hatprofile.k"),
json_mesh_file=Path("data/hat/cached_hat_key.json"))
```
### Add element features
```python
m2v.add_features_from_ansa(
["aspect", "warpage"],
Path("data/hat/Hatprofile.k"),
json_mesh_file=Path("data/hat/cached_hat_key.json"))
```
### Aggregate
```python
import numpy as np
m2v.aggregate("aspect", [0,2,3], np.nanmean)
```
### Extract Feature Vectors
```python
m2v.to_dataframe()
```
### Optional: Visualize a single aggregated feature on mesh
```python
m2v.get_visualization_plotly("aspect-nanmean-2")
```
