V&V&UQ in Engineering

In engineering, proper V&V&UQ can be a matter of life and death. Every engineer is trained with the knowledge that human lives may depend on the systems that he or she helps to design, build, or maintain. V&V&UQ are the newest tool-set designed to help engineers live up to that awesome responsibility.

Sometimes, only a few hundreds of millions of dollars worth of equipment are on the line. The stakes vary project-by-project. What is universal to all engineering projects is that engineers use quantitative models to help guide their design, production, and operational decisions. If model predictions are wrong - either because the model was improperly executed mathematically, or because the model itself is wrong, or because uncertainties in the model parameters were un-represented or under-represented in the risk analysis - then bad things can happen. On a good day, only money and valuable equipment are lost. On a bad day, lives are lost.

For generations, engineers have done the best they can with best-guess analysis. And, for the most part, engineers have done amazingly well. Nevertheless, since the second half of the 20th century, our civilization has been pushing the boundaries with successively larger and more daring engineering projects. And the best-guess approach to engineering analysis has faltered many times. The purpose of V&V&UQ - and the reason it has been the focus of so much research in the engineering community for the past thirty years - is that it will put engineering analysis on a more solid footing.

Alexandria Validation Consulting, LLC exists (in part) to help make a new generation of large-scale engineering projects possible. We do this by providing the V&V&UQ tools necessary to support safety analysis on those systems. That being said, our interests and methods are by no means limited to system-level analysis. Large-scale systems consist of successively smaller components. Every problem solved leads to new insights and new approaches. We are interested in any engineering problem of any scale in which traditional risk analysis methods have failed to yield a sensible and trustworthy answer.