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Accepted Manuscripts

BASIC VIEW  |  EXPANDED VIEW
Technical Brief  
Patrick J. Roache
J. Verif. Valid. Uncert   doi: 10.1115/1.4037706
Suggestions are made for modification and extension of the methodology and interpretations for ASME V&V 20-2009, Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer. A more conservative aggregation of numerical uncertainty into the total validation uncertainty is proposed. A more easily evaluated estimated bound on model error is suggested for the condition where the validation exercise results in large total validation uncertainty. Explicit distinctions between quality of the model and quality of the validation exercise are discussed. Extending the domain of validation for applications is treated by interpolating/extrapolating model error and total validation uncertainty, and adding new uncertainty from the new simulation at the application point. Model form uncertainty and epistemic uncertainties in general, while sometimes important in model applications, are argued to not be important issues in validation.
research-article  
Jerry A. McMahan, Brian J. Williams, Ralph C. Smith and Nicholas Malaya
J. Verif. Valid. Uncert   doi: 10.1115/1.4037705
We describe a framework for the verification of Bayesian model calibration routines. The framework is based on linear regression and can be configured to verify calibration to data with a range of observation error char- acteristics. The framework is designed for efficient implementation and is suitable for verifying code intended for large-scale problems. We propose an approach for using the framework to verify Markov chain Monte Carlo (MCMC) software by combining it with a non-parametric test for distribution equality based on the energy statistic. Our MATLAB-based reference implementation of the framework is shown to correctly distinguish between output obtained from correctly and incorrectly implemented MCMC routines. Since correctness of output from an MCMC software depends on choosing settings appropriate for the problem-of-interest, our framework can potentialy be used for verifying such settings.
research-article  
Pras Pathmanathan, Richard Gray, Vicente J. Romero and Tina Morrison
J. Verif. Valid. Uncert   doi: 10.1115/1.4037671
Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically-relevant predictions, direct validation of predictions may be impossible for ethical, technological or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibility of biomedical model predictions. Therefore, when evaluating biomedical models it is critical to rigorously assess applicability, that is, the relevance of the computational model and its validation evidence to the proposed context of use. However, there are no well-established methods for assessing applicability. Here we present a novel framework for performing applicability analysis, and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. The proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.
TOPICS: Biomedicine, Physics, Matter, Computer simulation, Engineering disciplines, Medical devices, Modeling

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