Flavour current correlators and the non-Abelian hydrodynamic approximation: the charged sector

What the math gives to ML

The paper gives an explicit rational response function for charged non-Abelian modes: the isospin chemical potential shifts the frequency by ω±μ₃, while longitudinal dynamics contain both a precessional pole and a diffusion pole. This is a transferable recipe for initializing or constraining sequence and spatiotemporal neural modules with physically meaningful oscillatory-diffusive spectra rather than unconstrained learned recurrences. The most practical adaptation is a Fourier-mode state-space layer whose poles are parameterized by a learnable chemical-potential frequency and nonnegative diffusion rate, optionally mixed with a standard residual branch. Its benefit is falsifiable on long-context or spatiotemporal benchmarks through improved stability and loss at fixed parameter count.