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.

Agenda:

• 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

Featured products

All products mentioned in this webinar are developed by MathWorks.

• MATLAB
• Simulink
• Deep Learning Toolbox
• Statistics and Machine Learning Toolbox

How to Access:

To watch the webinar, simply fill out the form on this page. Once registered, you’ll gain immediate access and can watch it directly on the page. Additionally, you’ll receive an email with a link to access the on-demand session at your convenience.