Non-intrusive and structure preserving multiscale integration of stiff ODEs, SDEs and Hamiltonian systems with hidden slow dynamics via flow averaging
arXiv:0908.1241 · doi:10.1137/090771648
Abstract
We introduce a new class of integrators for stiff ODEs as well as SDEs. These integrators are (i) {\it Multiscale}: they are based on flow averaging and so do not fully resolve the fast variables and have a computational cost determined by slow variables (ii) {\it Versatile}: the method is based on averaging the flows of the given dynamical system (which may have hidden slow and fast processes) instead of averaging the instantaneous drift of assumed separated slow and fast processes. This bypasses the need for identifying explicitly (or numerically) the slow or fast variables (iii) {\it Nonintrusive}: A pre-existing numerical scheme resolving the microscopic time scale can be used as a black box and easily turned into one of the integrators in this paper by turning the large coefficients on over a microscopic timescale and off during a mesoscopic timescale (iv) {\it Convergent over two scales}: strongly over slow processes and in the sense of measures over fast ones. We introduce the related notion of two-scale flow convergence and analyze the convergence of these integrators under the induced topology (v) {\it Structure preserving}: for stiff Hamiltonian systems (possibly on manifolds), they can be made to be symplectic, time-reversible, and symmetry preserving (symmetries are group actions that leave the system invariant) in all variables. They are explicit and applicable to arbitrary stiff potentials (that need not be quadratic). Their application to the Fermi-Pasta-Ulam problems shows accuracy and stability over four orders of magnitude of time scales. For stiff Langevin equations, they are symmetry preserving, time-reversible and Boltzmann-Gibbs reversible, quasi-symplectic on all variables and conformally symplectic with isotropic friction.
69 pages, 21 figures
References in corpus (2)
Cited by in corpus (11)
- Kernel Flows: from learning kernels from data into the abyss
- Gamblets for opening the complexity-bottleneck of implicit schemes for hyperbolic and parabolic ODEs/PDEs with rough coefficients
- Dynamic mode decomposition for multiscale nonlinear physics
- High order variational integrators in the optimal control of mechanical systems
- Simply improved averaging for coupled oscillators and weakly nonlinear waves
- Accelerated sampling by infinite swapping of path integral molecular dynamics with surface hopping
- Accurate and Efficient Simulations of Hamiltonian Mechanical Systems with Discontinuous Potentials
- Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations
- Computing Singularly Perturbed Differential Equations
- NySALT: Nyström-type inference-based schemes adaptive to large time-stepping
- A Dilation-based Seamless Multiscale Method For Elliptic Problems