Inertial manifold explained
In mathematics, inertial manifolds are concerned with the long term behavior of the solutions of dissipative dynamical systems. Inertial manifolds are finite-dimensional, smooth, invariant manifolds that contain the global attractor and attract all solutions exponentially quickly. Since an inertial manifold is finite-dimensional even if the original system is infinite-dimensional, and because most of the dynamics for the system takes place on the inertial manifold, studying the dynamics on an inertial manifold produces a considerable simplification in the study of the dynamics of the original system.[1]
In many physical applications, inertial manifolds express an interaction law between the small and large wavelength structures. Some say that the small wavelengths are enslaved by the large (e.g. synergetics). Inertial manifolds may also appear as slow manifolds common in meteorology, or as the center manifold in any bifurcation. Computationally, numerical schemes for partial differential equations seek to capture the long term dynamics and so such numerical schemes form an approximate inertial manifold.
Introductory Example
Consider the dynamical system in just two variables
and
and with parameter
:
[2]
- It possesses the one dimensional inertial manifold
of
(a parabola).
- This manifold is invariant under the dynamics because on the manifold
which is the same as
attracts all trajectories in some finite domain around the origin because near the origin
(although the strict definition below requires attraction from all initial conditions).
Hence the long term behavior of the original two dimensional dynamical system is given by the 'simpler' one dimensional dynamics on the inertial manifold
, namely
.
Definition
Let
denote a solution of a dynamical system. The solution
may be an evolving vector in
or may be an evolving function in an infinite-dimensional
Banach space
.
In many cases of interest the evolution of
is determined as the solution of a differential equation in
, say
with initial value
.In any case, we assume the solution of the dynamical system can be written in terms of a
semigroup operator, or state transition matrix,
such that
for all times
and all initial values
.In some situations we might consider only discrete values of time as in the dynamics of a map.
An inertial manifold[1] for a dynamical semigroup
is a smooth
manifold
such that
is of finite dimension,
for all times
,
attracts all solutions exponentially quickly, that is, for every initial value
there exist constants
such that
.
The restriction of the differential equation
to the inertial manifold
is therefore a well defined finite-dimensional system called the
inertial system.
[1] Subtly, there is a difference between a manifold being attractive, and solutions on the manifold being attractive.Nonetheless, under appropriate conditions the inertial system possesses so-called
asymptotic completeness:
[3] that is, every solution of the differential equation has a companion solution lying in
and producing the same behavior for large time; in mathematics, for all
there exists
and possibly a time shift
such that
dist(S(t)u0,S(t+\tau)v0)\to0
as
.
Researchers in the 2000s generalized such inertial manifolds to time dependent (nonautonomous) and/or stochastic dynamical systems (e.g.[4] [5])
Existence
Existence results that have been proved address inertial manifolds that are expressible as a graph.[1] The governing differential equation is rewritten more specifically in the form
for unbounded self-adjoint closed operator
with domain
, and nonlinear operator
.Typically, elementary spectral theory gives an orthonormal basis of
consisting of eigenvectors
:
,
, for ordered eigenvalues
.
For some given number
of modes,
denotes the projection of
onto the space spanned by
, and
denotes the orthogonal projection onto the space spanned by
.We look for an inertial manifold expressed as the graph
.For this graph to exist the most restrictive requirement is the
spectral gap condition[1]
}+\sqrt) where the constant
depends upon the system.This spectral gap condition requires that the spectrum of
must contain large gaps to be guaranteed of existence.
Approximate inertial manifolds
Several methods are proposed to construct approximations toinertial manifolds,[1] including theso-called intrinsic low-dimensional manifolds.[6] [7]
The most popular way to approximate follows from theexistence of a graph.Define the
slow variables
, and the 'infinite'
fast variables
.Then project the differential equation
onto both
and
to obtain the coupled system
and
.
For trajectories on the graph of an inertialmanifold
, the fastvariable
.Differentiating and using the coupled system form gives thedifferential equation for the graph:
\left[Ap+Pf(p+\Phi(p))\right]
+A\Phi(p)+Qf(p+\Phi(p))=0.
This differential equation is typically solved approximatelyin an asymptotic expansion in 'small'
togive an invariant manifold model,
[8] or a nonlinear Galerkin method,
[9] both of which use a global basis whereas the so-called
holistic discretisation uses a local basis.
[10] Such approaches to approximation of inertial manifolds arevery closely related to approximating
center manifoldsfor which a web service exists to construct approximationsfor systems input by auser.
[11] See also
References
- R. Temam. Inertial manifolds. Mathematical Intelligencer, 12:68–74, 1990
- Roberts . A. J. . Simple examples of the derivation of amplitude equations for systems of equations possessing bifurcations . Journal of the Australian Mathematical Society, Series B . Cambridge University Press (CUP) . 27 . 1 . 1985 . 0334-2700 . 10.1017/s0334270000004756 . 48–65. free .
- Robinson . James C . The asymptotic completeness of inertial manifolds . Nonlinearity . IOP Publishing . 9 . 5 . 1996-09-01 . 0951-7715 . 10.1088/0951-7715/9/5/013 . 1996Nonli...9.1325R . 1325–1340. 250890338 .
- Schmalfuss . Björn . Schneider . Klaus R. . Invariant Manifolds for Random Dynamical Systems with Slow and Fast Variables . Journal of Dynamics and Differential Equations . Springer Science and Business Media LLC . 20 . 1 . 2007-09-18 . 1040-7294 . 10.1007/s10884-007-9089-7 . 2008JDDE...20..133S . 133–164. 123477654 .
- Pötzsche . Christian . Rasmussen . Martin . Computation of nonautonomous invariant and inertial manifolds . Numerische Mathematik . Springer Science and Business Media LLC . 112 . 3 . 2009-02-18 . 0029-599X . 10.1007/s00211-009-0215-9 . 449–483. 6111461 .
- Maas . U. . Pope . S.B. . Simplifying chemical kinetics: Intrinsic low-dimensional manifolds in composition space . Combustion and Flame . Elsevier BV . 88 . 3–4 . 1992 . 0010-2180 . 10.1016/0010-2180(92)90034-m . 239–264.
- Bykov . Viatcheslav . Goldfarb . Igor . Gol'dshtein . Vladimir . Maas . Ulrich . On a modified version of ILDM approach: asymptotic analysis based on integral manifolds . IMA Journal of Applied Mathematics . Oxford University Press (OUP) . 71 . 3 . 2006-06-01 . 1464-3634 . 10.1093/imamat/hxh100 . 359–382.
- Roberts . A. J. . The Utility of an Invariant Manifold Description of the Evolution of a Dynamical System . SIAM Journal on Mathematical Analysis . Society for Industrial & Applied Mathematics (SIAM) . 20 . 6 . 1989 . 0036-1410 . 10.1137/0520094 . 1447–1458.
- Foias . C. . Jolly . M.S. . Kevrekidis . I.G. . Sell . G.R. . Titi . E.S. . On the computation of inertial manifolds . Physics Letters A . Elsevier BV . 131 . 7–8 . 1988 . 0375-9601 . 10.1016/0375-9601(88)90295-2 . 1988PhLA..131..433F . 433–436.
- A. J.. Roberts. A holistic finite difference approach models linear dynamics consistently. Mathematics of Computation. 72. 241. 247–262. 2002-06-04. 10.1.1.207.4820. 10.1090/S0025-5718-02-01448-5. 11525980.
- Web site: Construct centre manifolds of ordinary or delay differential equations (autonomous).