Episode
Design For Real World Engineering: Integrating Uncertainty Into Product Assessment - Greg Grigoriadis - Metisec
- Published
- May 11, 2026
- Duration seconds
- 1023
- Processing state
not_requested- Canonical source
- https://www.designforam.com/p/design-for-real-world-engineering
Actions
POST https://stenobird.com/v1/public/podcasts/cdfam-computational-design-symposium-7077587/episodes/design-for-real-world-engineering-integrating-uncertainty-into-product-assessment-greg-grigoriadis-metisec/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587/design-for-real-world-engineering-integrating-uncertainty-into-product-assessment-greg-grigoriadis-metisec.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com