Reduced Order Modeling

Reduce computational complexity of models by creating accurate surrogates

Reduced order modeling is a technique for reducing the computational complexity or storage requirements of a model while preserving the expected fidelity within a satisfactory error. Working with a surrogate reduced order model can simplify analysis and control design.

Topics

Reduced Order Modeling Basics

Reduced Order Modeling
Reduce computational complexity of models by creating accurate surrogates.

Data-Driven Methods

Nonlinear ARX Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a nonlinear ARX model.
Hammerstein-Wiener Model of SI Engine Torque Dynamics
This example describes modeling the nonlinear torque dynamics of a spark-ignition (SI) engine as a Hammerstein-Wiener model.
Neural State-Space Model of SI Engine Torque Dynamics
This example describes reduced order modeling (ROM) of the nonlinear torque dynamics of a spark-ignition (SI) engine using a neural state-space model.
Reduced Order Modeling of Electric Vehicle Battery System Using Neural State-Space Model
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.

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
Identify a linear parameter varying reduced order model of a cascade of nonlinear mass-spring-damper systems.