---
license: cc-by-4.0
language:
  - en
pretty_name: LoGIC-Dataset
tags:
  - tpms
  - metamaterials
  - porous-structures
  - mechanical-metamaterials
  - finite-element-analysis
  - computational-fluid-dynamics
  - permeability
  - stress-strain
  - surrogate-modeling
  - logic-design
size_categories:
  - 100<n<1K
---

# LoGIC-Dataset

LoGIC-Dataset is a research dataset associated with the LoGIC-Design framework for heterogeneous triply periodic minimal surface (TPMS) architectures.

The dataset supports data-driven performance prediction of heterogeneous TPMS porous architectures generated using low-dimensional control grids. It includes geometry-related data and simulation-derived labels for mechanical and transport property prediction.

## Associated code

The source code of the LoGIC-Design framework is publicly available at:

https://github.com/pxy1118/LoGIC

The archived software release is available on Zenodo:

https://doi.org/10.5281/zenodo.20411608

## Dataset description

The dataset contains heterogeneous TPMS architectures generated using the LoGIC-Design framework. The framework encodes topology distribution, lattice orientation, and local density using compact 3 × 3 × 3 control grids.

The dataset includes two main groups of simulation-derived labels:

- Mechanical response data obtained from finite element analysis, including stress–strain response labels.
- Transport property data obtained from computational fluid dynamics simulations, including permeability and pressure-drop-related labels.

In the associated manuscript, the mechanical response dataset contains 200 heterogeneous TPMS structures, and the permeability dataset contains 600 heterogeneous TPMS structures.

## Intended use

This dataset is intended for:

- Data-driven prediction of mechanical response in heterogeneous TPMS architectures.
- Permeability and pressure-drop prediction for porous structures.
- Benchmarking surrogate models for structure–property prediction.
- Reproducible research on heterogeneous porous metamaterials.
- Future inverse design and human–AI collaborative design of TPMS-based architectures.

## Citation

If you use this dataset, please cite the associated manuscript and the dataset DOI.

Associated software DOI:

https://doi.org/10.5281/zenodo.20411608

Dataset DOI:

https://doi.org/10.57967/hf/8949
## License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

## Contact

Xiaoyue Pan  
College of Information Engineering, Zhejiang University of Technology  
ORCID: 0009-0005-6098-5571