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Learn to quantify uncertainties in computer models through this comprehensive lecture by Merlin Keller from EDF R&D, delivered at IPhT-TV. Explore fundamental concepts and methodologies for understanding and measuring uncertainties that arise in computational modeling across various scientific and engineering applications. Discover how to identify different sources of uncertainty in computer simulations, including parametric uncertainties, model form uncertainties, and numerical uncertainties. Examine statistical and probabilistic approaches for uncertainty propagation and sensitivity analysis in complex computational systems. Gain insights into practical techniques for uncertainty quantification including Monte Carlo methods, polynomial chaos expansions, and surrogate modeling approaches. Understand how to interpret and communicate uncertainty results effectively for decision-making processes in industrial and research contexts. Delve into real-world applications and case studies from EDF's research and development work, demonstrating how uncertainty quantification impacts energy sector modeling and risk assessment. Master the theoretical foundations while learning practical implementation strategies for incorporating uncertainty analysis into your own computational modeling workflows.
