Jrnl of Verification Validation & Uncertainty Quan

The Journal of Verification, Validation and Uncertainty Quantification (VVUQ) disseminates original and applied research, illustrative examples, and high quality validation experimental data as applied to: Design of experiments; Computational models; and Analysis of experimental results. The journal is cross cutting and serves an audience of engineers and scientists. We encourage authors of papers that describe discipline specific models and experiments to consider formulating their papers in two parts, the discipline specific part to be published in their home journals and the part describing the validation and uncertainty aspects of their work that would be published in the VV&UQ Journal.

SCOPE
Areas of interest including, but not limited to: Code verification; Solution verification; Validation; Uncertainty quantification; Model prediction; Model adequacy; Model accuracy; Predictive capacity; Model maturity; Phenomena identification and ranking table (PIRT); Design of experiments; Experimental uncertainty; Uncertainty in measurement; Model uncertainty; Model discrepancy; Sensitivity analysis; Model fidelity; Intended use; Context of use; Regulatory science; Aleatoric uncertainty; Epistemic uncertainty; Comparator; Quantification of margins and uncertainties (QMU); Fundamentals of probability; Applications of probability; Bayesian inference

Papers submitted to the Journal must address one or more of the following areas:

Code Verification, Calculation Verification, Validation, and Uncertainty Quantification

Code Verification: the assurance that code outputs converge to analytical solutions, particularly in terms of the rate of reduction of discretization errors (i.e., the order of accuracy). Examples of such verification often use the method of manufactured solutions as well as analytical solutions.

Calculation Verification: the estimation of numerical errors in simulation models due to discretization (typically time and/or space), incomplete iterative convergence, statistical convergence, and response surface approximations. Discretization errors are commonly estimated using Richardson extrapolation with systematic mesh refinement, residual-based methods (e.g., error transport equations, defect correction, adjoint methods), or by increasing the order of accuracy of the basis function representations.

Validation: the adequacy of a model to represent the reality of interest. Papers that focus on validation must involve assessment of models (e.g., by estimating model form uncertainty) through comparison to physical observations (i.e., experimentation). Acceptable comparisons require that both experimental and simulation results be accompanied by relevant measures of uncertainty from both sources.

Uncertainty Quantification (UQ): includes both the propagation of input/parametric uncertainty through models to the outputs of interest as well as methods for aggregating and conveying uncertainty from different sources (input/parametric uncertainty, numerical uncertainty, model form uncertainty). It is expected that papers that focus on uncertainty propagation will use modern and well defined statistical approaches to quantify this source of uncertainty. As UQ is a fundamental component of V&V, papers addressing this topic are a major aim of the journal.

Published: Quarterly - March, June, September, December