PRD-Specialized AI v1.5 : A Quantum Relational Framework for Emergent Spacetime Geometry and Einstein-Like Dynamics

This work introduces a PRD-specialized artificial intelligence architecture designed for the computational study of emergent spacetime geometry within the Pattana-Relational Dynamics (PRD) framework.Unlike conventional large language models or general-purpose machine learning systems, the proposed AI is physics-native, operating directly on quantum relational operators rather than statistical data representations. The system integrates symbolic quantum algebra, graph neural networks (GNNs), and physics-informed optimization to simulate relational dynamics and emergent gravitational structure.Within the PRD framework, spacetime geometry is not assumed as a fundamental background but arises from correlations between relational operators acting on a relational Hilbert space. These operators obey the SU(5) Lie algebra, representing the full set of causal relational conditions. The AI architecture encodes these operators as nodes in a graph structure, with edges representing algebraic correlations derived from commutation relations.The system computes relational covariant derivatives, constructs emergent metric tensors from operator correlations, and derives curvature quantities including the Riemann tensor, Ricci tensor, and an Einstein-like tensor. Physics-informed loss functions enforce essential physical constraints, including SU(5) commutation relations, the contracted Bianchi identity, and convergence to Einstein’s field equations in the macroscopic limit.A scale-dependent weighting mechanism enables the model to bridge Planck-scale quantum dynamics and classical gravitational behavior, allowing the system to learn emergent spacetime structure across multiple physical scales.The framework includes a complete simulation pipeline, from operator initialization and quantum state preparation to metric generation, curvature computation, and neural network optimization. Python implementations using PyTorch and graph neural network libraries provide a computational environment for numerical experiments.This approach establishes a new direction for AI-assisted quantum gravity research, enabling computational exploration of relational spacetime emergence, quantum gravitational phenomenology, and causal relational modeling beyond the capabilities of conventional machine learning systems.