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Validation is often defined as the process of determining the degree to whicha model is an accurate representation of the real world from the perspective ofits intended uses. Validation is crucial as industries and governments dependincreasingly on predictions by computer models to justify their decisions. Inthis article, we survey the model validation literature and propose toformulate validation as an iterative construction process that mimics theprocess occurring implicitly in the minds of scientists. We thus offer a formalrepresentation of the progressive build-up of trust in the model, and therebyreplace incapacitating claims on the impossibility of validating a given modelby an adaptive process of constructive approximation. This approach is betteradapted to the fuzzy, coarse-grained nature of validation. Our procedurefactors in the degree of redundancy versus novelty of the experiments used forvalidation as well as the degree to which the model predicts the observations.We illustrate the new methodology first with the maturation of QuantumMechanics as the arguably best established physics theory and then with severalconcrete examples drawn from some of our primary scientific interests: acellular automaton model for earthquakes, an anomalous diffusion model forsolar radiation transport in the cloudy atmosphere, and a computational fluiddynamics code for the Richtmyer-Meshkov instability. This article is anaugmented version of Sornette et al. [2007] that appeared in Proceedings of theNational Academy of Sciences in 2007 (doi: 10.1073/pnas.0611677104), with anelectronic supplement at URLhttp://www.pnas.org/cgi/content/full/0611677104/DC1. Sornette et al. [2007] isalso available in preprint form at physics/0511219.

A General Strategy for Physics-Based Model Validation Illustrated with  Earthquake Phenomenology, Atmospheric Radiative Transfer, and Computational  Fluid Dynamics