Prometheus: Integration of the Lorenz Chaotic System into Bio-Inspired Neural Architectures. Toward a New AI for Biomedical Diagnostics and Aerospace Control

Abstract This paper introduces Prometheus, an experimental framework that integrates the Lorenz chaotic system into the architectural and training process of a neural network. Specifically, it proposes a special layer whose internal dynamics are governed by nonlinear differential equations, inducing non-stationary transformations of latent representations over the course of training epochs. Concurrently, an adaptive control module, inspired by the human brain, autonomously regulates the time step and the intensity of chaos, in order to preserve a state that is mathematically compatible with the convergence of the optimization via Backpropagation. The results suggest that this experimental framework may facilitate the development of more flexible AI architectures in contexts characterized by high variability, such as in the biomedical and aerospace sectors. Methodology: The model was scientifically validated through:Comparative Analysis (Imaging): Tests on CIFAR-10 and MRI with Rician noise (σ = 0.15), compared with standard CNN architectures (with/without dropout).Exploratory Validation (Testing): Generalization test on a simulated lunar landing module under extreme conditions (lateral wind 5.0, turbulence 0.5, gravity 1.62 m/s², within the Gymnasium LunarLanderContinuous-v3 environment). Results: In the biomedical domain (CNN-MRI), Prometheus demonstrated minimal decision entropy (E = 0.018) with a p-value 50 times lower than the alpha threshold of 0.05 (p = 0.001***) and a 97% confidence level. On CIFAR-10, despite a 90% reduction in data and 50% noise, the model maintained a 4.4% advantage over standard models (p = 0.02**). The most significant results (p = 0.001***) were achieved thanks to algorithmic synergy between the algorithm Prometheus Engine and the integrated Natural Selection optimizer. An experimental internal self-control system that drives the algorithm’s evolution until optimal performance is achieved. Finally, to assess the model’s generalization ability, an experiment was conducted in an aerospace simulator designed for training autonomous lunar landers using ReinforcementLearning (RL). The task involved controlling a lander during landing on a rocky exomoon with a high atmospheric density. The lunar lander, equipped with Prometheus technology, demonstrated stable convergence, achieving a median reward of 48.59/200 over 220 episodes. This indicates that in 50% of cases, the lander not only managed to maintain proper flight stability, despite the prohibitive atmospheric conditions, but also succeeded in performing landings sufficiently thorough for the intended space mission. Manifold analysis further confirms that these results are not stochastic artifacts. The control trajectories learned by the model evolve, in fact, along nonlinear structures consistent with the internal dynamics induced by the Lorenz system. An interesting aspect that emerged from this analysis is that the system does not appear to perform traditional “online” computation, acting by trial and error, but rather through a sort of “rescaling” of phase space, that is, starting from a repertoire of pre-existing potential paths. Conclusions: The results show that the coupling of chaotic dynamics and selective optimization produces a transdisciplinary Large Effect Size. The investigations suggest that neural networks built using this framework do not act probabilistically but rather by making a choice that already exists, from a physical standpoint, even before it is processed at the neural level, thereby maximizing performance in data-scarce and highly variable scenarios. These results could, therefore, open the door to a new form of Deep Learning (DL) engineering, moving toward a biologically inspired General Artificial Intelligence (AGI).