This research introduces a Deep-Tech Artificial Intelligence architecture inspired by space mathematics, nonlinear dynamics, and control theory. Modern artificial intelligence systems largely function as prediction engines. However, real-world decision environments—such as financial markets, energy infrastructure, and geopolitical systems—operate under significantly different constraints, including uncertainty propagation, irreversible outcomes, systemic risk amplification, and governance limitations. To address these challenges, this study proposes a physics-grounded decision intelligence framework in which decision processes are modeled as dynamic state spaces governed by differential equations, potential fields, and stability conditions. The proposed architecture integrates multiple mathematical components, including: • decision state space modeling• nonlinear risk dynamics• irreversibility metrics• entropy-based uncertainty propagation• Lyapunov stability analysis• governance-constrained decision manifolds• optimal decision trajectory modeling Unlike conventional AI architectures focused solely on prediction accuracy, this framework prioritizes decision stability, controllability, and governance alignment in high-impact environments. The resulting system represents a new category of artificial intelligence infrastructure: physics-grounded decision intelligence, capable of supporting complex decision environments where irreversible outcomes and systemic risks must be carefully controlled. Potential application domains include: • financial risk modeling• energy investment analysis• geopolitical risk systems• enterprise decision infrastructure This research contributes to the emerging field of Deep-Tech AI, combining advanced mathematics, control theory, and artificial intelligence to create a scientifically grounded decision architecture.
YASIN KALAFATOGLU (Sat,) studied this question.