| | --- |
| | 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 |
