Spectral submanifold explained
In dynamical systems, a spectral submanifold (SSM) is the unique smoothest invariant manifold serving as the nonlinear extension of a spectral subspace of a linear dynamical system under the addition of nonlinearities.[1] SSM theory provides conditions for when invariant properties of eigenspaces of a linear dynamical system can be extended to a nonlinear system, and therefore motivates the use of SSMs in nonlinear dimensionality reduction.
SSMs are chiefly employed for the exact model reduction of dynamical systems. For the automated computation of SSMs and the analysis of the reduced dynamics, open source online software packages such as SSMTool and SSMLearn have been published. These tools allow to study system dynamics either from the underlying equations of motion or from trajectory data, supporting both analytical and data-driven approaches. Detailed documentation for SSMTool is provided online.[2]
Definition
Consider a nonlinear ordinary differential equation of the form
with constant matrix
and the nonlinearities contained in the smooth function
.
Assume that
for all eigenvalues
of
, that is, the origin is an asymptotically stable fixed point. Now select a span
of
eigenvectors
of
. Then, the eigenspace
is an invariant subspace of the linearized system
Under addition of the nonlinearity
to the linear system,
generally perturbs into infinitely many invariant manifolds. Among these invariant manifolds, the unique smoothest one is referred to as the spectral submanifold.
An equivalent result for unstable SSMs holds for
.
Existence
The spectral submanifold tangent to
at the origin is guaranteed to exist provided that certain non-resonance conditions are satisfied by the eigenvalues
in the spectrum of
.
[3] In particular, there can be no linear combination of
equal to one of the eigenvalues of
outside of the spectral subspace. If there is such an outer resonance, one can include the resonant mode into
and extend the analysis to a higher-dimensional SSM pertaining to the extended spectral subspace.
Non-autonomous extension
The theory on spectral submanifolds extends to nonlinear non-autonomous systems of the form
=Ax+f0(x)+\epsilonf1(x,\Omegat), \Omega\inTk, 0\le\epsilon\ll1,
with
a
quasiperiodic forcing term.
[4] Significance
Spectral submanifolds are useful for rigorous nonlinear dimensionality reduction in dynamical systems. The reduction of a high-dimensional phase space to a lower-dimensional manifold can lead to major simplifications by allowing for an accurate description of the system's main asymptotic behaviour.[5] For a known dynamical system, SSMs can be computed analytically by solving the invariance equations, and reduced models on SSMs may be employed for prediction of the response to forcing.[6]
Furthermore these manifolds may also be extracted directly from trajectory data of a dynamical system with the use of machine learning algorithms.[7]
See also
External links
Notes and References
- Haller . George . Ponsioen . Sten . 2016 . Nonlinear normal modes and spectral submanifolds: Existence, uniqueness and use in model reduction . Nonlinear Dynamics . 86 . 3 . 1493–1534 . 1602.00560 . 10.1007/s11071-016-2974-z . 44074026.
- Web site: SSMTool . 2024-11-12 . ssmtool.mae.sustech.edu.cn.
- Cabré . P. . Fontich . E. . de la Llave . R. . 2003 . The parametrization method for invariant manifolds I: manifolds associated to non-resonant spectral subspaces . Indiana Univ. Math. J. . 52 . 283–328 . 10.1512/iumj.2003.52.2245 . 2117/876. free .
- Haro . A. . de la Llave . R. . 2006 . A parameterisation method for the computation of invariant tori and their whiskers in quasiperiodic maps: Rigorous results . Differ. Equ. . 228 . 2 . 530–579 . 2006JDE...228..530H . 10.1016/j.jde.2005.10.005.
- Rega . Giuseppe . Troger . Hans . 2005 . Dimension Reduction of Dynamical Systems: Methods, Models, Applications . Nonlinear Dynamics . 41 . 1–3 . 1–15 . 10.1007/s11071-005-2790-3 . 14728580.
- Ponsioen . Sten . Pedergnana . Tiemo . Haller . George . 2018 . Automated computation of autonomous spectral submanifolds for nonlinear modal analysis . Journal of Sound and Vibration . 420 . 269–295 . 1709.00886 . 2018JSV...420..269P . 10.1016/j.jsv.2018.01.048 . 44186335.
- Cenedese . Mattia . Axås . Joar . Bäuerlein . Bastian . Avila . Kerstin . Haller . George . 2022 . Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds . Nature Communications . 13 . 1 . 872 . 10.1038/s41467-022-28518-y . 8847615 . 35169152. 2201.04976 . 2022NatCo..13..872C .