2025-2026 NNL Distinguished Lecturer 

Dr. William Oberkampf 

Consulting Engineer

Georgetown, Texas

Topic: Elements of Computational Predictive Capability

Abstract: Simulation is becoming the primary tool in predicting the performance, reliability, and safety of engineered systems. Terminology such as “virtual testing” and “full physics simulation” are extremely appealing when physical testing, budgets and schedules are highly constrained. From a simulation-informed decision-making perspective, the central question is; what is the evidence for simulation credibility? Many contend that higher fidelity physics modeling, combined with faster computers, is the path forward for improved simulation credibility. However, considering the rate of increase in physics complexity, computational requirements, and detailed model information requirements needed for finer scales of physics, the argument for simulation credibility is questionable. More convincingly, the last five decades of astounding growth in computing speed has not delivered reliable simulation credibility. This lecture argues that improved simulation credibility should be based on the building blocks of code verification, solution verification, model validation, and improved estimation of predictive uncertainty. Predictive uncertainty is the emerging field attempting to capture all sources of uncertainty in order to foretell the response of a system for conditions where no experimental data are available. Categories of uncertainty are model input uncertainty (which includes uncertainty in the environments and scenarios to which the system could be exposed), model form uncertainty (due to approximations and assumptions in the model), and numerical solution error. I argue that the estimation of predictive uncertainty is the most constructive and prudent path forward, particularly for high-consequence systems. To achieve a comprehensive estimation of total uncertainty, a distinction must be made between uncertainties that are random (aleatory uncertainties) and those that are due to lack of knowledge (epistemic uncertainties). Imprecise probability approaches can explicitly include both aleatory and epistemic uncertainties, thereby providing a forthright description of total predictive uncertainty for a decision maker or regulatory authority. The lecture will close with suggestions for research topics concerning VVUQ.

Bio: Dr. William Oberkampf has 55 years of experience in research and development in fluid dynamics, heat transfer, flight dynamics, solid mechanics, and structural dynamics. During the last 30 years, Dr. Oberkampf emphasized research and development in methodologies and procedures for verification, validation, and uncertainty quantification for a wide variety of applications. He has written over 195 journal articles, book chapters, conference papers, and technical reports; in addition to two books. He has taught 76 short courses, primarily in the fields of verification, validation, and uncertainty quantification. Dr. Oberkampf received his PhD in Aerospace Engineering in 1970 from the University of Notre Dame. Dr. Oberkampf served on the faculty of the Mechanical Engineering Department at the University of Texas at Austin and served in both staff member and management positions for 29 years at Sandia National Laboratories. Since this time, he has been a consultant to organizations in the U.S. and Europe. He is a fellow of the American Institute of Aeronautics and Astronautics and NAFEMS.

Thursday,  September 11, 2025 

102 BEH 

11:00am  

Guest Host: Reed Anderson - NNL