README.md

---
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: source_file
    dtype: string
  - name: question
    dtype: string
  - name: question_type
    dtype: string
  - name: question_id
    dtype: int32
  - name: answer
    dtype: string
  - name: answer_choices
    list: string
  - name: correct_choice_idx
    dtype: int32
  - name: image
    dtype: image
  - name: video
    dtype: video
  - name: media_type
    dtype: string
  splits:
  - name: test
    num_bytes: 187015546578
    num_examples: 102678
  download_size: 175022245655
  dataset_size: 187015546578
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
license: mit
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 100K<n<1M
---

# OpenSeeSimE-Structural: Engineering Simulation Visual Question Answering Benchmark

## Dataset Summary

OpenSeeSimE-Structural is a large-scale benchmark dataset for evaluating vision-language models on structural analysis simulation interpretation tasks. It contains over 100,000 question-answer pairs across parametrically-varied structural simulations including stress analysis, and deformation patterns.

## Purpose

While vision-language models (VLMs) have shown promise in general visual reasoning, their effectiveness for specialized engineering simulation interpretation remains largely unexplored. This benchmark enables:

- Statistically robust evaluation of VLM performance on engineering visualizations
- Assessment across multiple reasoning capabilities (captioning, reasoning, grounding, relationship understanding)
- Evaluation using different question types (binary classification, multiple-choice, spatial grounding)

## Dataset Composition

### Statistics
- **Total instances**: 102,678 question-answer pairs
- **Simulation types**: 5 structural models (Dog Bone, Hip Implant, Pressure Vessel, Beams, Wall Bracket)
- **Parametric variations**: 1,024 unique instances per base model (4^5 parameter combinations)
- **Question categories**: Captioning, Reasoning, Grounding, Relationship Understanding
- **Question types**: Binary, Multiple-choice, Spatial grounding
- **Media formats**: Both static images (1920×1440 PNG) and videos (Originally Extracted at: 200 frames, 29 fps, 7 seconds)

### Simulation Parameters

Each base model varies across 5 parameters with 4 values each:

**Dog Bone**: Length, Thickness, Diameter, Axial Load, Bending Load  
**Hip Implant**: Beam Length, Beam Diameter, Ball Diameter, Axial Load, Bending Load  
**Pressure Vessel**: Length, Thickness, Diameter, Material, Pressure  
**Thermal Beam**: Thickness, Bending Load, Young's Modulus, Tensile Yield Strength, Cross Section Shape  
**Wall Bracket**: Length, Width, Height, Thickness, Bending Force

### Question Distribution

- **Binary Classification**: 40% (yes/no questions about symmetry, stress types, uniformity, etc.)
- **Multiple-Choice**: 30% (4-option questions about deformation direction, stress dominance, magnitude ranges, etc.)
- **Spatial Grounding**: 30% (location-based questions with labeled regions A/B/C/D)

## Data Collection Process

### Simulation Generation
1. Base models sourced from Ansys Mechanical tutorial files
2. Parametric automation via PyMechanical and PyGeometry interfaces
3. Systematic variation across 5 parameters with 4 linearly-spaced values
4. All simulations solved using finite element analysis with validated convergence settings

### Ground Truth Extraction
Automated extraction eliminates human annotation costs and ensures consistency:

- **Statistical Analysis**: Direct queries on result arrays (max, min, mean, std)
- **Distribution Analysis**: Threshold-based classification using coefficient of variation
- **Physics-Based Classification**: Stress tensor analysis and mechanics principles
- **Spatial Localization**: Color-based region generation with computer vision algorithms

All ground truth derived from numerical simulation results rather than visual interpretation.

## Preprocessing and Data Format

### Image Processing
- Resolution: 1920×1440 pixels
- Format: PNG with lossless compression
- Standardized viewing orientations: front, back, left, right, top, bottom, isometric
- Consistent color mapping: rainbow gradients (red=maximum, blue=minimum)
- Automatic deformation scaling (1.5× relative to maximum dimension)

### Video Processing
- 200 frames at 29 fps (7 seconds duration)
- Maximum deformation at frame 100 (temporal midpoint)
- H.264 compression at 1920×1440 resolution
- Uniform frame sampling for model input (32 frames)

### Data Fields
```python
{
    'file_name': str,           # Unique identifier
    'source_file': str,         # Base simulation model
    'question': str,            # Question text
    'question_type': str,       # 'Binary', 'Multiple Choice', or 'Spatial'
    'question_id': int,         # Question identifier (1-20)
    'answer': str,              # Ground truth answer
    'answer_choices': List[str], # Available options
    'correct_choice_idx': int,  # Index of correct answer
    'image': Image,             # PIL Image object (1920×1440)
    'video': Video,             # Video frames
    'media_type': str          # 'image' or 'video'
}
```

## Labels

All labels are automatically generated from simulation numerical results:

- **Binary questions**: "Yes" or "No"
- **Multiple-choice**: Single letter (A/B/C/D) or descriptive option
- **Spatial grounding**: Region label (A/B/C/D) corresponding to labeled visualization locations

Label generation employs domain-specific thresholds:
- Uniformity: CV ≤ 0.2 (20%)
- Symmetry: 60% of node pairs within 10% tolerance (structural)
- Spatial matching: 50-pixel separation for region placement

## Dataset Splits

- **Test split only**: 102,678 instances
- No train/validation splits provided (evaluation benchmark, not for model training)
- Representative sampling across all simulation types and question categories

## Intended Use

### Primary Use Cases
1. **Benchmark evaluation** of vision-language models on engineering simulation interpretation
2. **Capability assessment** across visual reasoning dimensions (captioning, spatial grounding, relationship understanding)
3. **Transfer learning analysis** from general-domain to specialized technical visual reasoning

### Out-of-Scope Use
- Real-time engineering decision-making without expert validation
- Safety-critical applications without human oversight
- Generalization to simulation types beyond structural mechanics

## Limitations

### Technical Limitations
- **Objective tasks only**: Excludes subjective engineering judgments requiring domain expertise
- **Single physics domain**: Structural mechanics only (see OpenSeeSimE-Fluid for fluid dynamics)
- **Ansys-specific**: Visualizations generated using Ansys Mechanical rendering conventions
- **Static parameters**: Fixed material properties and boundary conditions per instance
- **2D visualizations**: All inputs are 2D projections of 3D simulations

### Known Biases
- **Color scheme dependency**: Questions exploit default rainbow gradient conventions
- **Geometry bias**: Selected simulation types may not represent full diversity of structural analysis applications
- **View orientation bias**: Standardized camera positions may not capture all critical simulation features

## Ethical Considerations

### Responsible Use
- Models evaluated on this benchmark should NOT be deployed for safety-critical engineering decisions without expert validation
- Automated interpretation should augment, not replace, human engineering expertise
- Users should verify that benchmark performance translates to their specific simulation contexts

### Data Privacy
- All simulations contain no proprietary or confidential engineering data
- No personal information collected
- Publicly available tutorial files used as base models

### Environmental Impact
- Dataset generation required significant computational CPU resources
- Consider environmental cost of large-scale model evaluation on this benchmark

## License

MIT License - Free for academic and commercial use with attribution

## Citation

If you use this dataset, please cite:

```bibtex
@article{ezemba2024opensesime,
  title={OpenSeeSimE: A Large-Scale Benchmark to Assess Vision-Language Model Question Answering Capabilities in Engineering Simulations},
  author={Ezemba, Jessica and Pohl, Jason and Tucker, Conrad and McComb, Christopher},
  year={2025}
}
```

## AI Usage Disclosure

### Dataset Generation
- **Simulation automation**: Python scripts with Ansys PyMechanical interface
- **Ground truth extraction**: Automated computational protocols (no AI involvement)
- **Quality validation**: Expert oversight of automated extraction procedures
- **No generative AI** used in dataset creation, labeling, or curation

### Visualization Generation
- Ansys Mechanical rendering engine (deterministic, physics-based)
- Standardized color mapping and camera controls
- No AI-based image generation or enhancement

## Contact

**Authors**: Jessica Ezemba (jezemba@andrew.cmu.edu), Jason Pohl, Conrad Tucker, Christopher McComb  
**Institution**: Department of Mechanical Engineering, Carnegie Mellon University  

## Acknowledgments

- Ansys for providing simulation software and tutorial files
- Carnegie Mellon University for computational resources
- Reviewers and domain experts who validated the automated extraction protocols

---

**Version**: 1.0  
**Last Updated**: December 2025  
**Status**: Complete and stable