Model error refers to the discrepancy between the true values or behavior of a system and the values predicted by a mathematical model. This type of error can arise due to simplifications or assumptions made during the modeling process, inaccuracies in parameter estimation, or limitations of the numerical methods used. Understanding model error is crucial for assessing the reliability of numerical solutions in inverse problems, where the goal is often to infer unknown parameters from observed data.
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