  This work proposes an adaptive structure-preserving model order reduction
method for finite-dimensional parametrized Hamiltonian systems modeling
non-dissipative phenomena. To overcome the slowly decaying Kolmogorov width
typical of transport problems, the full model is approximated on local reduced
spaces that are adapted in time using dynamical low-rank approximation
techniques. The reduced dynamics is prescribed by approximating the symplectic
projection of the Hamiltonian vector field in the tangent space to the local
reduced space. This ensures that the canonical symplectic structure of the
Hamiltonian dynamics is preserved during the reduction. In addition, accurate
approximations with low-rank reduced solutions are obtained by allowing the
dimension of the reduced space to change during the time evolution. Whenever
the quality of the reduced solution, assessed via an error indicator, is not
satisfactory, the reduced basis is augmented in the parameter direction that is
worst approximated by the current basis. Extensive numerical tests involving
wave interactions, nonlinear transport problems, and the Vlasov equation
demonstrate the superior stability properties and considerable runtime speedups
of the proposed method as compared to global and traditional reduced basis
approaches.
