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AI with Model-Based Design: Reduced Order Modeling

Overview

Large-scale, high-fidelity nonlinear models can take hours or even days to simulate. In fact, system analysis and design may require thousands or hundreds of thousands of simulations to obtain meaningful results, causing a significant computational challenge for many teams. Moreover, linearizing complex models can result in high-fidelity models that do not contribute to the dynamics of interest in your application. In such situations, you can use reduced order models to significantly speed up simulations and analysis of higher-order large-scale systems.

In this session, you will learn how to speed up the simulation of a complex model (a vehicle engine) by replacing a high-fidelity model with a reduced order model (ROM) leveraging Artificial Intelligence techniques. These data-driven methods use input-output data from the original high-fidelity first-principles model to construct a ROM that accurately represents the underlying system. You will see how different approaches may be explored (Machine Learning, Deep Learning, System Identification), to choose the most appropriate AI technique in terms of overall system design, from model accuracy to deployment efficiency.

Highlights

  • Designing and training machine learning components with Statistics and Machine Learning Toolbox
  • Designing and training deep learning components with Deep Learning Toolbox
  • Designing and training components with System Identification Toolbox
  • Integrating machine learning and deep learning models into Simulink for system-level simulation
  • Generating optimized C code and performing HIL tests

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

Product Focus

AI with Model-Based Design: Reduced Order Modeling

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