Deterministic engineering analysis assigns fixed values to loading conditions, geometry, and material properties. The approach is tractable, but it forces a choice between conservative overdesign and exposure to failure modes that fall outside assumed limits. Neither outcome is satisfying.
Greg Grigoriadis of Metisec describes a design toolkit that replaces fixed inputs with statistical distributions and runs Monte Carlo simulations across the resulting parameter space. The output is a probabilistic picture of performance: failure probabilities, sensitivity rankings, and the specific conditions that actually drive risk.
A sensor mounting bracket for a smart wearable serves as the test case. Traditional optimization cut bracket weight by 30%. Probabilistic analysis revealed the design had been tuned to an improbable drop event and still carried unresolved thermal failure risk. Incorporating that information allowed the team to re-optimize and achieve a 50% weight reduction at a demonstrably low failure probability.
Greg is an engineering consultant specializing in consumer electronics, digital twin technologies, and advanced simulation workflows. His practice combines physics-based modeling with data-driven methods across finite element analysis, predictive maintenance, and automated computation pipelines.
Presented at CDFAM Barcelona 2026. Learn more at cdfam.com.
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