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Computational microstructure design aims to fully exploit the precipitate strengthening potential of an alloy system. The development of accurate models to describe the temporal evolution of precipitate shapes and sizes is of great technological relevance. The experimental investigation of the precipitate microstructure is mostly based on two-dimensional micrographic images. Quantitative modeling of the temporal evolution of these microstructures needs to be discussed in three-dimensional simulation setups. To consistently bridge the gap between 2D images and 3D simulation data, we employ the method of central moments. Based on this, the aspect ratio of plate-like particles is consistently defined in two and three dimensions. The accuracy and interoperability of the method is demonstrated through representative 2D and 3D pixel-based sample data containing particles with a predefined aspect ratio. The applicability of the presented approach in integrated computational materials engineering (ICME) is demonstrated by the example of γ″ microstructure coarsening in Ni-based superalloys at 730 °C. For the first time, γ″ precipitate shape information from experimental 2D images and 3D phase-field simulation data is directly compared. This coarsening data indicates deviations from the classical ripening behavior and reveals periods of increased precipitate coagulation. 

Abstract Environmental decisions with substantial social and environmental implications are regularly informed by model predictions, incurring inevitable uncertainty. The selection of a set of model predictions to inform a decision is usually based on model performance, measured by goodness‐of‐fit metrics. Yet goodness‐of‐fit metrics have a questionable relationship to a model's value to end users, particularly when validation data are themselves uncertain. For example, decisions based on flow frequency models are not necessarily improved by adopting models with the best overall goodness of fit. We propose an alternative model evaluation approach based on the conditional value of sample information, first defined in 1961, which has found extensive use in sampling design optimization but which has not previously been used for model evaluation. The metric uses observations from a validation set to estimate the expected monetary costs associated with model prediction uncertainties. A model is only considered superior to alternatives if (i) its predictions reduce these costs and (ii) sufficient validation data are available to distinguish its performance from alternative models. By describing prediction uncertainties in monetary terms, the metric facilitates the communication of prediction uncertainty by end users, supporting the inclusion of uncertainty analysis in decision making.