UMOPWAI - Unified Model of Physics With AI - An Ontological, Epistemic-Operational and Relational Model of Physical Reality: From the Classification of Reality to Bottom-Up Knowledge and a New Interaction Between Artificial Intelligence and Scientifi

UMOPWAI (Unified Model of Physics With AI) integrates the ontological model UMOSU, the epistemic-operational model UMOKWAI, and the relational metalanguage UCLIP into a unified architecture designed to organize knowledge of physical reality and enable artificial intelligence systems to operate on a phenomenal, relational, and verifiable basis. The model starts from a fundamental premise: physical reality constitutes a unified system in which phenomena, components, and interactions coexist. Scientific disciplines represent historical and methodological organizations of knowledge, but they do not necessarily constitute the ontological structure of reality. UMOSU identifies phenomena through domain-specific ontological thresholds. UMOKWAI organizes knowledge through scenarios, physical parameters, and Relational Knowledge Units (RKUs). UCLIP provides a common metalanguage for formalizing physical relations and recognizing quantities which, despite being represented by different symbols, share the same physical meaning. The architecture makes it possible to move beyond an exclusively discipline-based, top-down organization of teaching, proposing instead a bottom-up approach that starts from phenomena, identifies their characteristics, establishes the relevant parameters, defines validity scenarios, and retrieves the applicable relational knowledge. Artificial intelligence thus assumes a dual function: using structured and verifiable knowledge when a controlled answer is required, while retaining its generative capacity when formulating hypotheses, analogies, and new interpretations. The objective is not to make LLMs exclusively deterministic, but to distinguish the generation of hypotheses from verified knowledge. Keywords: UMOPWAI, UMOSU, UMOKWAI, UCLIP, ontology, phenomena, ontological thresholds, scenarios, RKU, relational language, bottom-up learning, artificial intelligence, hallucinations.