Practice

Training and validating a surrogate honestly, knowing its limits, and putting it to work in design.

• Training: Fit the weights once on the training simulations and watch the validation curve.
• Validation: Compare surrogate and solver on designs the network never saw, inside and outside the training range.
• Limits and trust: A surrogate is only as good as its data: know the domain, quantify error, verify the winner.
• Design use: Millisecond answers turn design studies, optimisation and real-time tools into routine work.
• Get started: From open frameworks and datasets to a surrogate trained on your own simulations.