Uncertainty, Validation of Computer Models and the Myth of Numerical Predictability

This publication addresses the issues of modeling, uncertainty quantification, model validation and numerical predictability. With the increasing role of numerical simulation in science, technology as well as every day decision-making, assessing the predictive accuracy of computer models becomes essential. Conventional approaches such as finite element model updating or Bayesian inference are undeniably useful tools but they do not fully answer the question: How accurately does the model represent reality? First, the evolution of scientific computing and consequences in terms of modeling and analysis practices are discussed. The intimate relationship between modeling and uncertainty is explored by defining uncertainty as an integrate part of the model, not just parametric variability or the lack of knowledge about the physical system being investigated. Examples from nuclear physics and structural dynamics are provided to illustrate issues related to uncertainty, validation and predictability. Finally, feature extraction or the characterization of the dynamics of interest from time series is discussed.