What is a potential consequence of a model not passing validation?

When a model does not pass validation, it indicates that the model's predictions or results do not align closely enough with real-world data or expected outcomes. As a consequence, the model may indeed require redesign or adjustment to improve its accuracy and reliability. This could involve altering assumptions, adjusting parameters, incorporating additional data, or even rethinking the model structure entirely to better capture the underlying phenomena it is intended to represent.

Validation serves as a critical checkpoint in the modeling process, ensuring that any conclusions drawn from the model can be trusted. Without successful validation, one cannot assume that the model will behave as expected in practical applications, making redesign or adjustment imperative for achieving more reliable results. This reinforces the importance of validation as a fundamental aspect of model development in engineering analysis and computation.