This webinar outlines how ML/ROM models shorten the simulation lifecycle across all industries. Discover the benefits of real-time prediction using image-based machine learning for any industry and any application.
In the design phase, CAE provides limited synergies between design & engineering, production, manufacturing, deployment, maintenance, and retirement/recycling. ML/ROM has the potential to speed up the development of tools that allow non-experts to use sophisticated simulation capabilities to increase productivity, optimize the computational resources required for the simulations, and improve the product design process through new insights. This webinar outlines how ML/ROM models shorten the simulation lifecycle across all industries.
This powerful combination of ML/ROM and a physics-based simulation approach is well-positioned to address better the increasingly complex design problems confronting design engineers today. Watch this webinar to learn how to enable real-time engineering solutions.
Key takeaways include:
- Learn how design and optimization engineers use a new set of tools coupled with Multiphysics simulations to make decisions for real-time or embedded analysis
- See how optimization and stochastic analysis of any complex model can be used for improving designs, minimizing costs, and reducing environmental impacts
- Understand how the ML/ROM models are the core of the “digital twin” concept and solutions
- Discover the benefits of real-time prediction using image-based machine learning for any industry and any application
About the Speakers:
Kambiz Kayvantash – Sr. Director of AI/ML Applications for Design and Engineering – Hexagon
Kambiz has over 40 years of industrial and academic experience and has undertaken research and industrial investigations for hundreds of aeronautic and automotive OEMs and Tier 1 companies. He is currently European Expert for AI/ML & road transport safety and continues to provide lectures at various academic institutes.
Rani Harb – Solution Lead for Materials & Data – Hexagon
Rani graduated from UCLA with a PhD in Computational Mechanics. His areas of expertise are material modeling, FEA and machine learning. He is currently working at Hexagon as a Solution Lead for Materials & Data where he leads the development of artificial intelligence and machine learning methodologies as applied to material prediction and structural performance.
This webinar is sponsored by Hexagon.