.. _glossary: ======== Glossary ======== Definitions of terms used throughout the NeurEco documentation. .. glossary:: :sorted: Build The process of training a NeurEco model. During build, the algorithm automatically determines the optimal network architecture and parameters. Checkpoint A saved state during training that captures intermediate models. Checkpoints allow resuming training and selecting models with different accuracy/complexity trade-offs. Classifier A Tabular model that predicts discrete categories (classes) from input features. Outputs are class probabilities. Complex output An output value with both real and imaginary components. Used in PFS for frequency response data (magnitude and phase). Compressor A Tabular model that reduces data dimensionality. The encoder compresses data; the decoder reconstructs it. Compute capability NVIDIA's measure of GPU features. NeurEco requires compute capability 7.5 or higher for GPU acceleration. Convolutional Dynamic A Dynamic model that applies a convolutional neural network to a sliding window over past input values to predict outputs. Requires uniform time steps. Called **CNN Dynamic** in the GUI; its Python class is ``ConvolutiveDynamic``. ConvolutiveDynamic The Python class of the Convolutional Dynamic model (see :term:`Convolutional Dynamic`). Denominator size In PFS models, the complexity of the rational function approximation. Larger denominator size captures more complex frequency behavior. Dynamic solution NeurEco solution for time-series data where past values influence future predictions. Includes ODE and Convolutional Dynamic approaches. EDNN File extension for NeurEco Tabular models (.ednn = embedded deep neural network). ECNN File extension for Convolutional Dynamic models (.ecnn = embedded convolutional neural network). EFNN File extension for PFS models (.efnn = embedded frequency neural network). Embed license Add-on license enabling export to deployment formats (ONNX, FMU, C, VBA). Enrichment In PFS, the iterative process of adding complexity to the rational approximation to improve accuracy. ERNN File extension for ODE models (.ernn = embedded recurrent neural network). Evaluate Running inference on a trained model to generate predictions for new input data. Excitation In Dynamic models, the input signal that drives the system. Also called input or stimulus. Export Converting a NeurEco model to a deployment format (ONNX, FMU, C header, VBA). FMU Functional Mock-up Unit. A standard format for simulation model exchange, widely used in automotive and industrial applications. Gradient The partial derivatives of model outputs with respect to inputs. Used for optimization and sensitivity analysis. Hidden state In ODE models, internal variables that capture system dynamics not directly observable in inputs/outputs. Initialization In Dynamic models, providing initial time steps to establish the system state before prediction begins. L2 relative error A measure of prediction accuracy: the L2 norm of prediction errors divided by the L2 norm of actual values. Lower is better. Neuron A computational unit in the neural network. NeurEco automatically determines the optimal number of neurons. Normalization Scaling data to a standard range. NeurEco supports various normalization strategies (mean shift, min-max scaling, etc.). ODE Ordinary Differential Equation. The ODE model learns hidden states that evolve according to differential equations. ONNX Open Neural Network Exchange. A portable format for machine learning models supported by many frameworks and runtimes. Output The predicted value(s) from a model. In Tabular, outputs are features; in Dynamic, outputs are time-series values. PFS Parametric Frequency Sweep. NeurEco solution for frequency-domain modeling with complex-valued outputs. Rational function A ratio of polynomials. PFS uses rational functions to approximate frequency response behavior. Regressor A Tabular model that predicts continuous numerical values from input features. Sensitivity How much a model output changes in response to input changes. Sensitivity analysis reveals which inputs most influence outputs. Solution A NeurEco problem type: Tabular, Dynamic, or PFS. Each solution addresses different data and modeling scenarios. Steady state In Dynamic models, the equilibrium condition where system outputs remain constant for constant inputs. Tabular solution NeurEco solution for structured data where each sample is independent. Includes Regressor, Classifier, and Compressor. Template A model type within a solution: Regression, Classification and Compression (Tabular), ODE and Convolutional Dynamic (Dynamic), and PFS. The CLI takes the template name first (e.g., ``neureco Regression build build.conf``). Trajectory In Dynamic models, a complete time-series sequence from start to end. Training typically uses multiple trajectories. Validation data Data held out from training to monitor model performance and prevent overfitting. VBA Visual Basic for Applications. Export format for using NeurEco models in Microsoft Excel. Window size In Convolutional Dynamic models, the number of past time steps used to predict the current output (at least 3). File Extensions Reference ========================= .. list-table:: :header-rows: 1 :widths: 20 30 50 * - Extension - Solution - Description * - ``.ednn`` - Tabular - Regressor, Classifier, Compressor models * - ``.ernn`` - Dynamic ODE - ODE models with hidden states * - ``.ecnn`` - Dynamic Convolutional - Convolutional Dynamic sliding-window models * - ``.efnn`` - PFS - Parametric Frequency Sweep models * - ``.onnx`` - Tabular - ONNX export format * - ``.fmu`` - All - FMU export format * - ``.h`` - Tabular - C header export * - ``.bas`` - Tabular - VBA export for Excel Module Import Reference ======================= .. list-table:: :header-rows: 1 :widths: 35 65 * - Import - Purpose * - ``from NeurEco import NeurEcoTabular as tab`` - Tabular solution (Regressor, Classifier, Compressor) * - ``from NeurEco import NeurEcoDynamic as dyn`` - Dynamic solution (ODE, ConvolutiveDynamic) * - ``from NeurEco import NeurEcoFrequential as freq`` - PFS solution * - ``from NeurEco import NeurEcoError`` - Base exception class