Update README.md

README.md

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+---
+
+# 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 ([email protected]), 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