Leadership Profile: ESTECO on the Value of AI-Powered Digital Engineering

The challenges of leveraging physics-based simulations throughout the engineering design process are well-known. Simulation is essential in product design, but can be extremely computationally intensive, making it impractical for real-time use and limiting its application. However, the growing need for accurate data earlier in the design phase is pushing OEMs to seek alternative approaches to shorten product development.

In this episode of DE Leadership Profile, a sponsored podcast series, Danilo Di Stefano, product manager for modeFRONTIER at ESTECO explains how adding AI data-driven modeling to modeFRONTIER software suite helps engineers deal with complex simulations. He also explains the advantages of using Proper Orthogonal Decomposition (POD) in Reduced Order Models (ROM) for high-dimensional data, and how the integration of Design of Experiments (DOE), Response Surface Models (RSM), and ROM improve the efficiency of AI and machine learning models.

“In this way, ESTECO’s AI data-driven modeling technology can significantly reduce the computation time needed when you perform design optimization”, Di Stefano said. “The AI-driven processes can automate various tasks, including design exploration, sensitivity analysis, and optimization … in this way accelerating the overall design process.”

For more information, visit www.esteco.com.

For the full interview, listen to the podcast below. (Intro and end music courtesy of Bensound.)

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