The ability to apply machine learning processes to engineering problems has revolutionized design and test across industries, but performing these processes can be challenging and time-consuming. However, a powerful software exists that can readily deploy machine learning and reduced order modeling techniques on simulation data, enabling engineers to reduce simulation and computation time from hours to seconds while maintaining high result-prediction accuracy. This 60-minute Webinar examines this software and the benefits to engineers — including faster experiment design, sensitivity analysis, animation prediction, test-data parameter fitting, and optimization — which reduce prototype simulation and product-development cycle time significantly.
This Webinar includes:
- An introduction to the software and its workflows for parametric modeling
- A look at its uses in multiple fields including structural analysis, computational fluid dynamics, multi-body dynamics, safety, manufacturing, and materials
- A demonstration of the software’s unique capabilities: handling complete transient and frequency-based responses, predicting animations and learning images, and CAD models
A Q&A session will follow the technical presentation.
Aditya Vipradas, Business Development Manager, Machine Learning Solutions, Hexagon
Aditya Vipradas is a professional business development and simulation engineer, who serves as the Business Development Manager of Machine Learning Solutions at Hexagon. He holds a master’s degree in mechanical engineering with a specialization in machine learning. Aditya is highly skilled in deploying various numerical tools to solve customer problems through reduced-order modeling, machine learning, and optimization. At Hexagon, through use of the ODYSSEE machine learning technology, he has enabled numerous customers to decrease their product development cycle time by reducing prototype simulation and testing.
Amanda Hosey, Editor, SAE Media Group