On the reliability of computed chaotic solutions of nonlinear differential equations
arXiv:0901.2986 · doi:10.1111/j.1600-0870.2009.00402.x
Abstract
In this paper a new concept, namely the critical predictable time , is introduced to give a more precise description of computed chaotic solutions of nonlinear differential equations: it is suggested that computed chaotic solutions are unreliable and doubtable when . This provides us a strategy to detect reliable solution from a given computed result. In this way, the computational phenomena, such as computational chaos (CC), computational periodicity (CP) and computational prediction uncertainty, which are mainly based on long-term properties of computed time series, can be completely avoided. So, this concept also provides us a time-scale to determine whether or not a particular time is long enough for a given nonlinear dynamic system. Besides, the influence of data inaccuracy and various numerical schemes on the critical predictable time is investigated in details by using symbolic computation software as a tool. A reliable chaotic solution of Lorenz equation in a rather large interval non-dimensional Lorenz time units is obtained for the first time. It is found that the precision of initial condition and computed data at each time-step, which is mathematically necessary to get such a reliable chaotic solution in such a long time, is so high that it is physically impossible due to the Heisenberg uncertainty principle in quantum physics. This however provides us a so-called "precision paradox of chaos", which suggests that the prediction uncertainty of chaos is physically unavoidable, and that even the macroscopical phenomena might be essentially stochastic and thus could be described by probability more economically.
29 pages, 13 figures, 1 table
References in corpus (2)
Cited by in corpus (26)
- On the mathematically reliable long-term simulation of chaos of Lorenz equation in the interval [0,10000]
- More than six hundreds new families of Newtonian periodic planar collisionless three-body orbits
- The 1223 new periodic orbits of planar three-body problem with unequal mass and zero angular momentum
- On the risks of using double precision in numerical simulations of spatio-temporal chaos
- Physical limit of prediction for chaotic motion of three-body problem
- Chaotic motion of three-body problem : an origin of macroscopic randomness of the universe
- On the numerical simulation of propagation of micro-level inherent uncertainty for chaotic dynamic systems
- Collisionless periodic orbits in the free-fall three-body problem
- Three-body problem -- from Newton to supercomputer plus machine learning
- Large-scale influence of numerical noises as artificial stochastic disturbances on a sustained turbulence
- On the origin of intrinsic randomness of Rayleigh-Benard turbulence
- Accurate predictions of chaotic motion of a free fall disk
- A kind of Lagrangian chaotic property of the Arnold-Beltrami-Childress flow
- A commend on "Three Classes of Newtonian Three-Body Planar Periodic Orbits" by Šuvakov and Dmitrašinović (PRL, 2013)
- Is a direct numerical simulation (DNS) of Navier-Stokes equations with small enough grid spacing and time-step definitely reliable/correct?
- On the inherent self-excited macroscopic randomness of chaotic three-body system
- Chaos: a bridge from microscopic uncertainty to macroscopic randomness
- One family of 13315 stable periodic orbits of the non-hierarchical unequal-mass triple system
- A comment on the arguments about the reliability and convergence of chaotic simulations
- A Self-Adaptive Algorithm of the Clean Numerical Simulation (CNS) for Chaos
- Noise-expansion cascade: an origin of randomness of turbulence
- Clean numerical simulation (CNS) of three-dimensional turbulent Kolmogorov flow
- Forward period analysis and the long term simulation of a periodic Hamiltonian system
- Physical significance of artificial numerical noise in direct numerical simulation of turbulence
- Parallelizing multiple precision Taylor series method for integrating the Lorenz system
- How Far Can We Trust Chaos? Extending the Horizon of Predictability