Poisson-GENERIC Neural Operators: Exact Metriplectic Structure in Function Space via Casimir Entropies

원문 초록

Existing thermodynamically consistent neural operators enforce GENERIC degeneracy by projecting the reversible operator, introducing state dependence without guaranteeing Jacobi. We instead use a compatible Lie--Poisson pencil $L=\alpha D+\lambda(uD+Du)$ for nonlinear transport and constant Poisson multipliers otherwise. Augmenting the observed field $u$ with a latent entropy density $s$ makes $S=\int s$ a Casimir, so $L\,\delta S/\delta z=0$ without projection. The energy combines a fixed mechanical quadratic, a learned gauge-free potential, and a convex internal energy; the friction operator $M=AA^\top$ enforces $M\,\delta E/\delta z=0$ pointwise and supports diffusion and damping without a separate transport generator. Skewness, positivity, and both degeneracies hold for any parameters; the continuum pencil satisfies Jacobi, verified on the alias-free band; the closed-form heat and damped-wave friction operators are representable; physical energy is bounded under coercivity assumptions; and a discrete-gradient integrator preserves both laws in discrete time. On four PDEs in 1D and 2D with three backbones (FNO, Transolver, CNO), at equal optimizer steps the model wins 62/72 seed-level comparisons against unconstrained baselines, recovers the exact transport and wave symbols, matches heat and Burgers dissipation within 12\%, and dissipates nothing on advection; at equal wall-clock the unconstrained FNO is the more accurate ten-frame surrogate. What the structure gives at any budget is the energy balance: over a hundred frames, in and out of distribution, the model never raises the physical energy and conserves it where dissipation vanishes, whereas the unconstrained FNO loses a quarter of a conserved energy off distribution and projected GENERIC-FNO creates energy on most frames. Late-time under-damping on heat and explicit-integrator divergence on the wave are reported as limits.