GENERIC-FNO: Embedding Energy Conservation and Entropy Production into Fourier Neural Operators
We propose GENERIC-FNO, a neural operator that embeds the metriplectic (GENERIC) degeneracy structure of nonequilibrium thermodynamics in function space, coupling reversible, energy-conserving dynamics to irreversible, entropy-producing dynamics via the degeneracy conditions. Prior structure-preserving neural operators enforce at most one conservation law or a Hamiltonian form, and thermodynamically consistent learning has been confined to finite-dimensional, graph, or particle systems. GENERIC-FNO learns the energy and entropy functionals as neural operators and builds the reversible and irreversible operators as diagonal Fourier multipliers flanked by rank-one projections that enforce b...
| Authors | Jason Sulskis, Sathya Ravi |
|---|---|
| Primary category | cs.LG |
| Published | 6 June 2026 |
| Last updated at source | 12 October 2026 |
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