{"podcast":{"title":"CDFAM Computational Design Symposium","slug":"cdfam-computational-design-symposium-7077587","podcast_index_feed_id":7077587,"rss_url":"https://api.substack.com/feed/podcast/798745.rss","website_url":"https://www.designforam.com/podcast","image_url":"https://substackcdn.com/feed/podcast/798745/f2c96dc0f73a38b9580cf546fbfb0454.jpg","author":"Duann Scott","episode_count":176,"summary":"Explorations of Computational Design, AI & Machine Learning for Engineering & Architecture","last_synced_at":"2026-09-22T14:17:07.753459+00:00","page_url":"https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587"},"episode":{"title":"Design For Real World Engineering: Integrating Uncertainty Into Product Assessment - Greg Grigoriadis - Metisec","slug":"design-for-real-world-engineering-integrating-uncertainty-into-product-assessment-greg-grigoriadis-metisec","published_at":"2026-05-11T13:30:14+00:00","page_url":"https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587/design-for-real-world-engineering-integrating-uncertainty-into-product-assessment-greg-grigoriadis-metisec","show_page_url":"https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587","url":"https://www.designforam.com/p/design-for-real-world-engineering","audio_url":"https://api.substack.com/feed/podcast/196575916/9593e4f77eee26b8c4c2e3b0d9d11399.mp3","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","meta_description":"Deterministic engineering analysis assigns fixed values to loading conditions, geometry, and material properties. The approach is tractable, but it forces…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1023,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"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","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587/design-for-real-world-engineering-integrating-uncertainty-into-product-assessment-greg-grigoriadis-metisec.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}