## UC Berkeley / Lawrence Berkeley Laboratory

#### V&V or a psychology of models

**Roger Ghanem, USC**

##### March 4th, 2010 at 2PM–3PM in 740 Evans Hall [Map]

Scientific discourse about Uncertainty Quantification, Verification and
Validation has been on a high note for the past decade. This fervor has been
driven to a large extent by the demand from various constituencies that
computational resources finally deliver on their promise of reproducing if not
predicting physical reality. Clearly, the value of such an achievement will be
enormous, ranging from superior product design to disaster mitigation and the
management of complex systems such as financial markets and SmartGrids. A
number of challenges are easily spotted on the path to delivering this
computational surrogate. First, reality itself is elusive and is not always
described with commensurate topologies. This problem is somewhat mitigated by
experimental techniques that can simultaneously measure physical phenomena at
multiple scales. Second, the mathematical model, which could include
algorithmic approximations for calibration and forward computations, introduces
additional assumptions that are often manifested as modeling and parametric
uncertainties. Third, and even when these models are accurate and well-resolved
numerically, uncertainties are introduced at the manufacturing stage when a
physical device is constructed to represent the physical device, allowing for
various tolerances and imperfections.

Physical reality, the mathematical model, its implementation into software
components, and the as-built device, describe an equivalence class of objects
that are expected to result in similar decisions. These can be viewed as
multiple personalities of the same entity, and V&V can be viewed as the art and
science of analyzing this divergence while enabling its successful resolution.

In this talk, I will describe our efforts as the psycho-analysis (and even
psycho-therapy) of models, that rely on the astute packaging of information in
accordance with the axioms of probability theory, and a function-analytic
approach for treating randomness.