Reduced Order Modeling
Use Deep Learning Toolbox™ for reduced order modeling tasks.
Reduced order modeling is a technique for reducing the computational complexity or storage requirements of a model while preserving its fidelity within an acceptable range of error. Working with a reduced order model can simplify control design and analysis. For example, you can replace computationally intensive subsystems in a Simulink® model with a trained neural network that makes realistic predictions.
You can create reduced order models (ROMs) of subsystems modeled in Simulink, including full-order, high-fidelity, third-party simulation models. You can also create ROMs using existing time-domain data.
The Reduced Order Modeler app provides a UI workflow for creating ROMs. To use the app, install the Reduced Order Modeler for MATLAB® Support Package by using the instructions in Get and Manage Add-Ons.
Apps
| Reduced Order Modeler | Create reduced order models based on Simulink models, subsystems within models, or simulation data (Since R2025b) |
Functions
exportNetworkToSimulink | Generate Simulink model that contains deep learning layer blocks and subsystems that correspond to deep learning layer objects (Since R2024b) |
Blocks
| Predict | Predict responses using a trained deep learning neural network |
| Stateful Predict | Predict responses using a trained recurrent neural network (Since R2021a) |
Topics
- Reduced Order Model of a Jet Engine Turbine Blade (System Identification Toolbox)
Create a ROM of a jet engine turbine blade, using the long short-term memory (LSTM) and NSS model types.
- Physical System Modeling Using LSTM Network in Simulink
This example shows how to create a reduced order model (ROM) that acts as a virtual sensor in a Simulink® model using a long short-term memory (LSTM) neural network.
- Reduced Order Modeling Using Continuous-Time Echo State Network
This example shows how to train a continuous-time echo state network (CTESN) model to solve Robertson's equation.
- Generate Deep Learning SI Engine Model (Powertrain Blockset)
Generate a deep learning SI engine model from measured transient engine data.
- Implement Unsupported Deep Learning Layer Blocks
This example shows how to implement layers using Simulink blocks or MATLAB code in a MATLAB Function block.
Related Information
- Reduced Order Modeling (Simulink)
- Reduced Order Modeling (System Identification Toolbox)
- Reduced Order
Modeling with MATLAB and Simulink
Featured Examples
Reduced Order Model of a Jet Engine Turbine Blade
Create a ROM of a jet engine turbine blade, using the long short-term memory (LSTM) and NSS model types.
Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
A reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model. The low-order model takes the current (charge or discharge) and state of charge (SOC) as inputs and predicts voltage and temperature of an electric vehicle (EV) battery module while the battery is being cooled by an edge-cooled plate with a coolant at a constant flow rate. You train the low-order model and deploy it in Simulink® to compare it against the high-fidelity model.
