Three-Dimensional Kardar--Parisi--Zhang Scaling in Polariton Condensates

What the math gives to ML

The paper derives an effective Kardar–Parisi–Zhang (KPZ) phase dynamics by eliminating fast density and reservoir modes from a driven-dissipative condensate, leaving diffusion, a nonequilibrium gradient-square nonlinearity, and white noise. Its transferable asset is a concrete coarse-grained latent-field dynamics with a measurable universality signature: in 3+1 dimensions, temporal and spatial roughness exponents should approach beta = 0.1845 and chi = 0.3135. A neural operator or state-space model can use a differentiable KPZ evolution block for spatiotemporal latent states, with learned coefficients and a falsifiable scaling-law diagnostic.