Breaking the Curse of Dimensionality: Computational Validation of Anomalous Diffusion Propulsion via Physics-Informed Neural Networks

Transport dynamics in high-dimensional hidden-variable theories, such as the Quantum Diffusion(DQ-12) framework, present numerical challenges that are often intractable via classical finiteelement methods. In this work, we present a solver based on Physics-Informed Neural Networks (PINNs) capable of solving the generalized Fokker-Planck equation in extended configuration spaces. We apply this model to an asymmetric resonant cavity to evaluate the hypothesisof thrust generation via information gradients. Our simulations reveal that a variable diffusivitymetric D(x), induced by the subspace geometry, generates a non-trivial pressure asymmetry. Weobserve a 58% increase in net thrust compared to a classical control with constant diffusivity.Furthermore, we analyze the physical implementation of this principle using confined MercuryPlasma (Hg), demonstrating that its high atomic mass and low ionization potential are criticalfor maximizing the inertial anchoring factor and propulsive efficiency (> 3 mN/kW).