Stress Testing to Harden Simulation Models and Uncover “Brittle” Behavior
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Challenge
Simulation models can exhibit unexpected or “brittle” behavior at the edges of their performance envelope, creating vulnerabilities that are difficult to find with standard testing. Proactively identifying these failure points is critical for building trust in the model.
Our Solution
We use OptDef’s optimization engine for “stress testing.” Instead of optimizing for a desirable outcome, we configure OptDef to search for a negative objective, such as maximizing miss distance in an area where the model is expected to have a high success rate.
Impact
In one instance recently this “adversarial” optimization approach successfully and automatically identified non-physical, discontinuous behaviors in an interceptor model. By pinpointing the exact conditions causing the failure, developers were able to update the model logic, resulting in a robust model with sensible performance mimicking real-world behavior across its entire engagement zone.
More Case Studies
Continuous Test & Evaluation for AI-Powered Fire Control
When developing an AI agent for a critical system, like an Integrated Air and Missile Defense (IAMD) fire control, developers must continuously verify that the model behaves correctly and that new updates do not introduce regressions. Manually testing the AI’s performance across all possible engagement scenarios is impossible.
Rapid Calibration of AI Models to High-Fidelity Benchmarks
High-fidelity, physics-based simulations (e.g., 6DOF trajectory models) are often too slow for large-scale analysis. Faster AI-based surrogate models are needed, but they must be accurately calibrated to match the “truth” performance of the accredited high-fidelity model.