Symplectic Neural Operators preserve Hamiltonian PDE structure, yielding long-term stability and superior energy behavior versus non-structure-preserving neural operators.
Standard approximation fails for discontinuous operators; encoder-decoders approximate maximally monotone operators via graph convergence, with resolvent-based parameterizations preserving maximal monotonicity.