A Resource Allocation Framework for Experiment-Based Validation of Numerical Models

In experiment-based validation, uncertainties and systematic biases in model predictions are reduced by either increasing the amount of experimental evidence available for model calibration—thereby mitigating prediction uncertainty—or increasing the rigor in the definition of physics and/or engineering principles—thereby mitigating prediction bias. Hence, decision makers must regularly choose between either allocating resources for experimentation or further code development. The authors propose a decision-making framework to assist in resource allocation strictly from the perspective of predictive maturity and demonstrate the application of this framework on a nontrivial problem of predicting the plastic deformation of polycrystals.

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