AI-based surrogate models enable engineers to evaluate far more design variants and operating conditions without running every possible combination through a high-fidelity simulation. However, conventional machine-learning models can produce plausible-looking results even when they encounter unfamiliar inputs. In safety-sensitive aerospace and defence applications, knowing where a model may be wrong is therefore just as important as the prediction itself.

This whitepaper demonstrates how probabilistic surrogate models combine fast predictions with numerical measures of uncertainty. In a case study involving an autonomous surveillance drone exposed to jamming signals, a model is trained using 210 targeted simulations instead of evaluating all 882 possible parameter combinations. It delivers full-field predictions within seconds to minutes and achieves an (R^2) of 95.4% on an independent test set.

In this white paper, you will learn:

• How surrogate models turn high-fidelity simulation data into full-field predictions within seconds to minutes
• Why conventional AI models can appear confident even when operating outside familiar data regions
• How probabilistic models quantify uncertainty alongside every prediction
• How uncertainty maps help identify where additional simulations, tests or data are required
• How the drone case study covers 882 possible jamming and absorber configurations using 210 targeted simulations
• How uncertainty-aware models support decisions from early development to mission operation
• How AI training and inference can remain entirely on-premise