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KAM & Symplectic Integration

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Workshop on KAM Theory and Geometric Integration , Banff, Canada (Organizers: Walter Craig, Erwan Faou, Benoit Grebert) 保结构算法构造及应用会议 (组织者:孙雅娟,王雨顺). Numerical Stability of Hamiltonian Systems by Symplectic Integration Zaijiu Shang AMSS, CAS, Beijing June 9, 2011, Banff 2011 年 12 月 7 日,北京.

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Workshop on KAM Theory and Geometric Integration,Banff, Canada (Organizers: Walter Craig, Erwan Faou, Benoit Grebert)保结构算法构造及应用会议(组织者:孙雅娟,王雨顺)

Numerical Stability of Hamiltonian Systems by Symplectic IntegrationZaijiu ShangAMSS, CAS, Beijing June 9, 2011, Banff


Here we consider the “numerical stability” without considering round-off errors, and assume the computer runs the discretized equations exactly.
kam symplectic integration
KAM & Symplectic Integration
  • The behavior of symplectic schemes should be and could be understood and analyzed in the context of KAM theory on preservation and break-down of invariant tori.

------ K. Feng, in the Proc. of the Conf. on Applied and Industry Mathematics, the Netherlands: Kluwer Academic Publisher, 1991. Ed. by R. Spigler.

(Channel and Scovel 1990, J. M. Sanz-serna 1994, …)

monographs on symplectic integration with kam theory
Monographs on Symplectic Integration with KAM Theory
  • E. Hairer, C. Lubich, and G. Wanner, Geometric Numerical Integration---Structure-Preserving Algorithms for Ordinary Differential Equations, Spinger-Verlag Berlin, 2002.
  • “K. Feng” and M. Qin, Symplectic Geometric Algorithms for Hamiltonian Systems, Zhejiang Science & Technology Publishing House, Hangzhou and Springer-Verlag Berlin 2010 (English revised version from the Chinese version 2003).
  • A fundamental problem of numerical methods for dynamical systems
  • Linear stability analysis
  • Nonlinear stability analysis
  • Backward analysis
  • Homoclinic/heteroclinic splitting under symplectic integrators
  • Conclusions
a fundamental problem of numerical methods for dynamical systems
A fundamental problem of numerical methods for dynamical systems
  • Poincaré: the problem of studying quasi-periodic motions of nearly integrable Hamiltonian systems is the fundamental problem of dynamics.
  • Yoccoz (1994): the goal of the theory of dynamical systems is to understand most of the dynamics of most systems.
  • Criterion by Yoccoz: the goal of the theory of numerical algorithms should be to design “proper” algorithms to preserve most of the dynamics of most systems.
  • Poincaré-Yoccoz's criterion: the problem of studying the “proper”algorithms to preserve most of the quasi-periodic motions of most nearly integrable Hamiltonian systems is a fundamental problem of numerical methods for dynamical systems.
typical dynamics of hamiltonian systems
Typical dynamics of Hamiltonian systems

Skeleton of dynamics

(Steady states in time-invariant sense):

  • Equilibrium: elliptic, hyperbolic, mixed…
  • Periodic solutions: closed trajectories
  • Quasi-periodic solutions: invariant tori,Cantori,…
  • Homoclinic/heteroclinic solutions: stable/unstable manifolds
  • Irregular solutions: diffusions (no recurrent property),…

Theme of the dynamical system:

  • Foliations of the phase space by (minimal) invariant manifolds/sets and the stability under perturbations?
example simple pendulum
Example: simple pendulum
  • Hamiltonian function:
  • Equations of motion:
  • Phase orbits:
  • Equilibria , elliptic (hyperbolic) for even (odd) k;
  • Closed curves for ;
  • Separatrix for
  • Foliation of the phase space:
explicit euler orbits expand outward wrong non symplectic expansive map
Explicit Euler: orbits expand outward (wrong!)(non-symplectic, expansive map)
implicit euler orbits contract inward wrong non symplectic contractive map
Implicit Euler: orbits contract inward (wrong!)(non-symplectic, contractive map)
midpoint rule almost circles for very long time right symplectic area preserving map
Midpoint rule: almost circles for “very long time”(right)(symplectic, area-preserving map)
midpoint rule saddle separatrix for very long time right symplectic area preserving map
Midpoint rule: saddle separatrix for “very long time”(right)(symplectic, area-preserving map)
  • h=0.5, N=105
linear stability analysis thinking in the dahlquist way
Linear Stabilityanalysis(Thinking in the Dahlquist way)
  • Test equation: Linear Hamiltonian systems having only stable solutions—Harmonic oscillators(unique up to coordinate transformations);
  • Symplectic methods apply to the test equation.
  • Analytic methods (RK, Multi-step, Composition): Dahlquist analysis works for scalar test equation in the complex plane!
  • Nonanalytic methods (PRK, Splitting, LIIIA-IIIB):

Harmonic oscillator systems in the real plane

Test Hamiltonian
  • Step transition map of numerical method applied to the test system
  • Stability matrix
Symplectic Euler:

where skis the k–th truncation of the formal solution of the Hamilton-Jacobian equation of type (P,q) and is initial value.

Lobatto III A-IIIB pairs:

---------Non-analytic symmetric methods

  • Conjecture (-Shang, Oberwolfach 2006, Focm2008):

(proved by McLachlan, Sun,etc without any quotation

linear stability analysis equilibrium analysis
Linear stability analysis---- equilibrium analysis
  • Analytic symplectic methods (SRK) have the whole real axis as the stability sets.
  • Non-analytic symplectic methods (PRK, Splitting) have only parts of the whole real axis as the stability sets.
  • Splitting symplectic methods with as big as possible stability set to apply in some chemical computation problems

(Casas, Murua,…)

linear stability analysis equilibrium analysis1
Linear stability analysis---- equilibrium analysis
  • All the existing methods preserve equilibrium points of the systems;
  • Symplectic methods preserve the type of elliptic equilibrium points and, therefore, preserve the linear stability of Hamiltonian systems at elliptic equilibrium points when the step size is in the stability set;
  • Symplectic methods preserve the type of hyperbolic equilibrium points and hence preserve chaotic structure of Hamiltonian systems near the hyperbolic equilibrium points except for some exceptional step sizes.
nonlinear stability analysis numerical kam theory
Nonlinear stability analysis ---Numerical KAM theory
  • Numerical KAM theorem (Shang 1999, 2000):
  • Preserve most of foliations by invariant tori of phase space of general completely integrable systems in measure sense;
  • Perpetual stability of any numerical solution of completely integrable systems of one degree of freedom by symplectic methods (e.g., simple pendulum);
  • Perpetual stability of most of numerical solutions of completely integrable systems of many degrees of freedom by symplectic methods in measure sense;
  • Time-step-size resonance occur when the degrees of freedom greater than 2.
Time-step size resonance set: for ,
  • Lemma 1: is dense in R for any !
  • This lemma shows that it seems not possible to compute invariant tori with any given frequency (even diophantine)

---------- contradict the numerical observations!

  • “KAM tori are very stiff” and can be simulated numerically stably.
diophantine step sizes
Diophantine step-sizes:
  • For any given Diophantine frequency ,
  • Diophentine step size set:
Large measure of the diophantine step size set:

if suitably choose the indecies and


It is easy to prove that “good” time step sizes are

for most dionphantine frequency vectors, which was already checked numerically by Faou etc.

(Oberwolfach workshop “Geometric numerical integration” ,

March 19-25, 2006, Numer. Math. 2008)

Quantitative estimates:
  • Numerical first integrals exist and

well defined and in involution on the union of the numerical invariant tori, in the Whitney’s sense, with large Lebesgue measure in the phase space. The numerical tori are the level sets of these first integrals.

is the backward Hamiltonian of the symplectic integrator.

Recent extensions of numerical KAM:

The results can be extended to nearly integrable Hamiltonian systems and systems with Ruessman’s nondegeneracy condition (Ding & -S, 2011).

More numerical KAM results:
  • Exponential (Effective) stability (Hairer & Lubich (1997)

--- in the exponentially close sense, numerical invariant tori exist and approximate the invariant tori of the discretized nearly integrable systems

  • Nekhoroshev stability (Moan (2004?, Ding, -S & Xie 2011)

---Any numerical solution is still “very stable” even after “very long” time steps.

  • Problem: the step size is required to be “very small” and stability conditions are checked “very difficultly”!

(dissipative invariant tori: Stoffer (1998), Hairer & Lubich (1999))

Nonlinear stability analysis in the KAM setting is numerically not useful!

Numerical KAM theory for Hamiltonian PDEs:

(E. Faou etc. and the French groups).

--- beyond my knowledge and understanding.

nonlinear analysis based on backward analysis
Nonlinear analysis based on backward analysis
  • There exists a Hamiltonian function which can be expressed as a formal power series in terms of the step

size such that the time- map of the formal phase flow

of this Hamiltonian system is identity to the symplectic integrator with time step size .

If the formal Hamiltonian is convergent, then the symplectic integrator preserves this Hamiltonian function.

In general, however, the formal Hamiltonian is divergent!

Numerical KAM convergent in a large set of the phase space!

McLachlan’s criterion of nonlinear stability

(BIT, 2004):

The level set of ( in stead, some finite truncation) is compact if original energy surface is compact.

(Example: nonlinear oscillation)

--- it is still difficult to give out an effective bound of time step size guaranteeing the numerical stability!

nonlinear stability analysis backward homoclinic analysis
Nonlinear stability analysis --- backward+homoclinic analysis
  • Backward/homoclinic criterion (-L. Song’s thesis, 2009):

--- Model example

  • Theorem. As the time step size increases from zero, the energy of the modified system at the augmented saddle point increases from minus infinity. When the energy equals to the energy at the preserved saddle point, then the homoclinic trajectory is totally destroyed. This gives a time step size bound which is not too small and when the time step size is below this bound, then there will be full of invariant curves inside the homoclinic trajectory for the modified system.
  • Symplectic Euler: h*=
Remark: as the truncation order of the modified system increases, the stability bound decreases. The convergent limit is the nonlinear stability bound of the symplectic integrator.
  • This bound can be easily verified for concrete symplectic integrators, and even can be applied to more general systems with stable motions.
homoclinic heteroclinic splitting under symplectic integrators
Homoclinic/heteroclinic splitting under symplectic integrators

Theorem (L. Song’s thesis, 2009). Assume that a Hamiltonian system of one degrees of freedom has a homoclinic trajectory. When a symplectic integrator applies to this system, then the homoclinic trajectory of the system will be split into stable and unstable trajectories of the integrator which intersect transversally in general and span a chaotic web with exponentially small maximal width with respect to the p-th power of time-step size of the integrator, where p is the accuracy order of the integrator.

  • Symplectic integration can simulate foliation structure of dynamics of integrable and nearly integrable Hamiltonian systems correctly;
  • In general, a homoclinic/heteroclinic trajectory (separatrix) of a Hamiltonian system splits under a symplectic integrator and forms a chaotic web of some width near the separatrix. It is fortunate, however, symplectic methods can give a smaller width of the chaotic web than other generally-purposed methods do.
  • Combining KAM theorem and the homoclinic splitting, we know that symplectic discretization generates numerical chaos, in general, but the chaotic part is very small in the phase space.