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August 8, 2022 16 min

Welcome to another episode of Engineer Innovation— a podcast for engineers, by engineers. Because we all want to build a better tomorrow, faster.


Simulation allows engineers to understand how their designs will perform under different circumstances. It is an essential part of any development process as it gives confidence to designs before the actual manufacturing can start.


Without simulation, it would be difficult to determine which combination of components and component design can give the best performance. The high number of different combinations that can be used for complex designs presents a big problem for engineers.


Machine learning is used to narrow down workable combinations, thereby, freeing up the engineers to test and improve what can work. It does this by analyzing existing data to create models that closely reflect what different combinations would result in.


Today, I’m joined by Justin Hodges, a Simcenter Machine Learning Tech Specialist.


Join us as we discuss how machine learning is used to accelerate the simulation process.


In this episode, you will learn:

  • The use of ML in simulation (00:53)
  • The benefits of using reduced order models (04:53)
  • An example where ML models have been successfully applied in simulation (10:06)
  • The best place to start if you wish to start using AI in simulation (12:01)

  • Connect with Justin Hodges:

  • LinkedIn
  • Siemens Simcenter

  • Hosted on Acast. See acast.com/privacy for more information.

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