Reduced Order Modeler for MATLAB provides an app for creating reduced order models (ROMs) of subsystems modeled in Simulink, including full-order, high-fidelity third-party simulation models. You can use reduced order models for system-level desktop simulation, hardware-in-the-loop (HIL) testing, control design, and virtual sensor modeling.
With the Reduced Order Modeler App, you can:
- Set up the design of experiments to generate input-output training data, or import pre-collected data from a full-order, high-fidelity subsystem
- Train and compare AI-based ROMs using pre-configured templates
- Export AI-based surrogate models to Simulink for system-level simulation, control design, and HIL testing
- Export ROMs as Functional Mockup Units (FMUs) for use outside of MATLAB and Simulink (with Simulink Compiler)
Design Experiments
Select Simulink signals and block parameters to use as ROM inputs, outputs, and parameters. Interactively design simulation experiments using built-in signal excitation types or specifying parameter values explicitly or via distributions. Specify boundaries for signal and parameter values to define the feasible design space and visualize its coverage.
Import Data for Training
Import existing time-domain data collected from a high-fidelity simulation model into the Reduced Order Modeler app for training reduced order models. Use data stored in matrices, timetables, or cell arrays of timetables and matrices.
Run Experiments
Run experiments sequentially or in parallel with Parallel Computing Toolbox and initiate model simulations. Visualize simulation results for signals and parameters of interest using built-in visualization plots.
Train Reduced Order Models
Create static or dynamic reduced order models using various networks. Automatically train and compare all available models, including Neural State Space, LSTM, MLP, and Nonlinear ARX models. Optimize hyperparameters sequentially or in parallel with Parallel Computing Toolbox to improve model fit. Compare accuracy metrics for trained models to select the optimal model for your application.
Use Reduced Order Models in Simulink
Bring trained ROMs into Simulink for system level simulation, control design, and HIL testing. Combine ROMs with first principles-based component models.
Deploy and Export Reduced Order Models
Deploy ROMs to embedded systems through automatic code generation. Export ROMs as FMUs (with Simulink Compiler) for use outside of MATLAB and Simulink.
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