Reduced Order Modeling
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.
You can create reduced order models (ROMs) of subsystems modeled in Simulink, including full-order, high-fidelity, third-party simulation models. You can use the ROMs you create for system-level desktop simulation, hardware-in-the-loop (HIL) testing, control design, and virtual sensor modeling.
The Reduced Order Modeler app provides a UI workflow for creating ROMs based on Simulink models or subsystems within models. 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) |
Topics
Reduced Order Modeling Basics
- Reduced Order Modeling Overview (System Identification Toolbox)
Reduce computational complexity of models by creating accurate surrogates.
Data-Driven Methods
- Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model (System Identification Toolbox)
This example shows 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. - Surrogate Modeling Using Gaussian Process-Based NLARX Model (System Identification Toolbox)
In this example, you replace a hydraulic cavitation cycle model in Simulink with a surrogate nonlinear ARX (NLARX) model to facilitate faster simulation. - Physical System Modeling Using LSTM Network in Simulink (Deep Learning Toolbox)
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.
Linearization-Based Methods
- LPV Approximation of Boost Converter Model (Simulink Control Design)
Approximate a nonlinear Simscape™ Electrical™ model using a linear parameter varying model. - Reduce Model Order Using Model Reducer App (Control System Toolbox)
Interactively reduce model order while preserving important dynamics. - Sparse Modal Truncation of Linearized Structural Beam Model (Control System Toolbox)
Compute a low-order approximation of a sparse state-space model obtained from linearizing a structural beam model. (Since R2023b) - Specify Linearization for Model Components Using System Identification (Simulink Control Design)
You can use System Identification Toolbox™ software to identify a linear system for a model component that does not linearize well, and use the identified system to specify its linearization. - Reduced Order Modeling of a Nonlinear Dynamical System as an Identified Linear Parameter Varying Model (System Identification Toolbox)
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems. - Approximate Nonlinear Behavior Using Array of LTI Systems (Simulink Control Design)
You can use linear parameter varying models to approximate the dynamics of nonlinear systems.
Physics-Based Methods
- Model an Excavator Dipper Arm as a Flexible Body (Simscape Multibody)
Use the Reduced Order Flexible Solid block to model a deformable body of arbitrary geometry. Start with the CAD geometry of the body, produce a finite-element mesh, and generate reduced-order data to use with the block. - Improve Simulation Speed of Power Electronics Systems with Reduced Order Modeling (Simscape Electrical)
This example shows how to enhance the model simulation speed of an electro-thermal DC-DC step-down converter by converting a high-fidelity switch to a reduced order model (ROM) switch. (Since R2024b)
Related Information
- Reduced Order Modeling (System Identification Toolbox)
- Configure Options in Reduced Order Modeler
- Reduced Order Modeling Discovery Page
