AI for Model-Based Design: Reduced Order Modeling

AI for Model-Based Design Reduced Order Modeling

This webinar is prerecorded

Free | online | Orkeny Zovathi

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Have you ever been in a situation where you got a fancy model of your system, but running it feels like trying to power a rocket with a potato battery. It’s slow, it’s clunky, and worst of all, it eats up precious time and resources.

In situations like this, you can use Reduced Order Modeling (ROM) to significantly speed up simulations and analysis of complex systems without compromising on accuracy. It reduces the dimensionality of a system, making it more manageable and efficient to simulate.

Of course, creating an effective ROM isn’t as simple as dropping a ball. Fortunately, it’s not as messy as dealing with a ball of fermions. This webinar will show you how to achieve faster simulations without the chaos and turn ROM into your secret weapon.

How the use of ROM is helpful to the engineers:

By distilling complex systems into simplified models, ROM streamlines the design process, making simulations faster, more efficient, and easier to manage. This means engineers can iterate more quickly, test out different scenarios, and ultimately arrive at optimized designs in less time.

How the use of ROM is helpful to the business:

By accelerating the design process, ROM helps companies bring products to market faster, giving them a competitive edge in rapidly evolving industries. Faster time-to-market means more opportunities to capture market share and stay ahead of the curve.


  • ROM overview
  • 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
  • Summary
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AI for Model-Based Design: Reduced Order Modeling

About the Speaker(s)

  • Orkeny Zovathi

    Orkeny Zovathi

    Application Engineer

    Orkeny is an application engineer at SciEngineer. His research and consulting work focus on AI and machine learning.

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Free | online | Orkeny Zovathi

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