  The expanding role of Artificial Intelligence (AI) in diverse engineering
domains highlights the challenges associated with deploying AI models in new
operational environments, involving substantial investments in data collection
and model training. Rapid application of AI necessitates evaluating the
feasibility of utilizing pre-trained models in unobserved operational settings
with minimal or no additional data. However, interpreting the opaque nature of
AI's black-box models remains a persistent challenge. Addressing this issue,
this paper proposes a science-based certification methodology to assess the
viability of employing pre-trained data-driven models in untrained operational
environments. The methodology advocates a profound integration of domain
knowledge, leveraging theoretical and analytical models from physics and
related disciplines, with data-driven AI models. This novel approach introduces
tools to facilitate the development of secure engineering systems, providing
decision-makers with confidence in the trustworthiness and safety of AI-based
models across diverse environments characterized by limited training data and
dynamic, uncertain conditions. The paper demonstrates the efficacy of this
methodology in real-world safety-critical scenarios, particularly in the
context of traffic state estimation. Through simulation results, the study
illustrates how the proposed methodology efficiently quantifies physical
inconsistencies exhibited by pre-trained AI models. By utilizing analytical
models, the methodology offers a means to gauge the applicability of
pre-trained AI models in new operational environments. This research
contributes to advancing the understanding and deployment of AI models,
offering a robust certification framework that enhances confidence in their
reliability and safety across a spectrum of operational conditions.
