Deep Learning Surrogates

Assembly Line

Deep learning in product design

πŸ“… Date:

✍️ Authors: Mickael Brossard, Jacomo Corbo, Marie Klaeyle, Bill Wiseman

πŸ”– Topics: Generative Design, Deep Learning Surrogates

🏒 Organizations: McKinsey

Digitization has also allowed engineers to give computers a more active role in the engineering process. Generative design and related optimization approaches work by programming a computer to run hundreds or thousands of simulations, tweaking the design between each run until it finds the best solution it can. The resulting geometries can outperform the work of the most experienced human designers.

At its outset, a deep learning surrogates (DLS) process looks a lot like other digital design optimization approaches. The engineering team defines the constraints and desired performance characteristics of the product, and the computer runs multiple conventional simulations on different design options. That’s where the approaches diverge, however.

As those initial simulations are run, they are used to train a neural network, which is set up to take the same inputs and attempts to replicate the outputs of the simulation system. When training is complete, this deep learning model will work just like the conventional simulation, but much, much faster. In real-world projects, deep learning simulation models can run orders of magnitude more quickly than their conventional counterparts.

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