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Glossary
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.. glossary::
   
   Activation Function
      A mathematical function that introduces non-linearity (curves, not just straight lines) into neural networks, enabling them to learn complex patterns.
   
   Aggregation
      Combining multiple values into one (e.g., taking the mean, sum, or maximum of neighbor features in a graph).
   
   Attention Mechanism
      A learned weighting system that determines which inputs are most important for making a prediction.
   
   Backpropagation
      The process of calculating gradients by working backwards through the network layers from output to input.
   
   B-Rep (Boundary Representation)
      A CAD model format that defines geometry and topology through surfaces, edges, and vertices, rather than point clouds or meshes. See :doc:`/programming_guide/cad-fundamentals` for details.
   
   Convolutional Neural Network (CNN)
      A type of neural network designed to process grid-structured data (images, UV grids) using learnable convolutional filters. Excels at detecting local patterns and spatial hierarchies.
   
   CustomFlowModel
      A flexible, modular framework that allows custom architectures or workflows for part analysis and feature recognition.
   
   Dihedral Angle
      The angle between two adjacent faces meeting at an edge, important for feature recognition. See :doc:`/programming_guide/cad-fundamentals` for computation details.
   
   Edge Features
      Numeric properties assigned to each edge in a graph (e.g., shared edge length, dihedral angle between adjacent faces).
   
   Embedding
      A learned representation that converts discrete items (like words or CAD entities) into continuous vectors of numbers.
   
   FabWave — 3D Part Repository
      A curated collection of CAD models used for training and benchmarking AI models in manufacturing and engineering contexts.
   
   Face Adjacency Graph
      A graph where nodes represent CAD faces and edges connect faces that share a common edge. Used by GNNs to learn from B-rep topology. See :doc:`/programming_guide/cad-fundamentals` for examples.
   
   Feature Recognition
      Identifying specific design or manufacturing features from 3D geometry and topology using graph-based learning.
   
   Feature Vector
      A fixed-size array of numbers that represents properties of an object (e.g., [area, perimeter, curvature] for a CAD face). Machine learning models process these numeric vectors instead of raw symbolic data.
   
   Gradient
      A mathematical measure of how much changing a weight would change the error - tells us which direction to adjust weights.
   
   Graph Neural Network (GNN)
      A type of neural network designed to process graph-structured data, where nodes represent entities (like CAD faces) and edges represent relationships (like face adjacency).
   
   GraphNodeClassification
      A graph-based deep learning method for recognizing local features (e.g., holes, fillets, chamfers) in the topological graph of a CAD model.
   
   Infoset (.parquet)
      A metadata-rich Parquet file containing supplementary information (labels, annotations, or material data) associated with each dataset.
   
   Logits
      The raw numerical scores output by a neural network before converting to probabilities (via softmax).
   
   Machine Learning Pipeline
      The complete process of data ingestion, preprocessing, model training, validation, and inference used to automate learning from 3D CAD data.
   
   Max Pooling
      Taking the maximum value from a group of values, used to reduce dimensionality while keeping the strongest signals.
   
   Mean Pooling
      Taking the average value from a group of values, used to aggregate information smoothly.
   
   Message Passing
      In graph neural networks, the process where each node collects and combines information from its neighboring nodes.
   
   Node Features
      Numeric properties assigned to each node in a graph (e.g., face area, surface type, curvature for a face-adjacency graph).
   
   Parts Classification
      Automatically categorizing components (e.g., brackets, gears, casings) using geometric and topological cues.
   
   Parts Segmentation
      Dividing a CAD model into semantically meaningful regions (e.g., faces, holes, slots) for downstream manufacturing analysis.
   
   Permutation Invariance
      A property where the output doesn't change regardless of input order - [A,B,C] gives the same result as [C,A,B].
   
   Pooling
      The process of downsampling data by combining neighboring values (e.g., max pooling takes the maximum, average pooling takes the mean). Reduces dimensionality while preserving important features.
   
   Pre-training
      Initial training on a large dataset before fine-tuning on a specific task, helping the model learn general patterns.
   
   Self-Supervised Learning
      Learning patterns from unlabeled data without human annotations, often by predicting hidden parts of the input.
   
   Storage Handler
      An abstraction layer for persisting encoded CAD data to different backends (memory, disk, cloud). See :doc:`/programming_guide/storage` for available implementations.
   
   Surface Normal
      A vector perpendicular to a surface at a given point, indicating the surface's orientation. Used as geometric features in ML models.
   
   Topology
      The relational structure connecting faces, edges, and vertices in a 3D model, describing which entities are adjacent or connected. Independent of exact shape or size.
   
   Training AI Model
      The phase where neural networks learn from labeled 3D parts to recognize shapes, classify components, or segment features.
   
   Translation Invariance
      A property where a neural network recognizes patterns regardless of their position in the input (e.g., detecting an edge anywhere in an image).
   
   U-Net
      A convolutional neural network (CNN) architecture commonly used for image or geometry segmentation, adapted here for 3D part segmentation.
   
   UV Grid
      A 2D grid of points sampled from a face surface using UV parameterization, creating an image-like representation suitable for CNNs.
   
   UV-Net
      A neural network architecture designed for solid classification in 3D CAD, leveraging both geometry (U) and topology (V) features for better understanding of parts.
   
   UV Parameterization
      A mapping from 2D parameter space (u,v) to 3D surface coordinates, enabling structured sampling of curved surfaces. See :doc:`/programming_guide/cad-fundamentals` for mathematical details.
   
   Zarr Format (.dataset)
      A chunked, compressed data format optimized for large multidimensional datasets, enabling efficient model training and storage.