Non-intrusive and structure preserving multiscale integration of stiff ODEs, SDEs and Hamiltonian systems with hidden slow dynamics via flow averaging
Molei Tao, Houman Owhadi, and Jerrold E. Marsden

TL;DR
This paper presents a versatile, non-intrusive multiscale integrator for stiff ODEs, SDEs, and Hamiltonian systems that preserves structure and efficiently captures slow dynamics without explicitly identifying fast variables.
Contribution
The authors introduce a new flow averaging-based integrator that is non-intrusive, structure-preserving, and convergent over multiple scales, applicable to a wide class of stiff dynamical systems.
Findings
Effective in Fermi-Pasta-Ulam problems over four orders of magnitude of time scales.
Preserves symplectic, time-reversible, and symmetry properties in Hamiltonian systems.
Applicable to stiff Langevin equations, maintaining key physical invariants.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
