Reversible dynamics

Reversible dynamics

Mathematics

In mathematics, a dynamical system is invertible if the forward evolution is one-to-one, not many-to-one; so that for every state there exists a well-defined reverse-time evolution operator.

The dynamics are time-reversible if there exists a transformation (an involution) π which gives a one-to-one mapping between the time-reversed evolution of any one state, and the forward-time evolution of another corresponding state, given by the operator equation:

:U_{-t} = pi , U_{t}, pi

Any time-independent structures (for example critical points, or attractors) which the dynamics gives rise to must therefore either be self-symmetrical or have symmetrical images under the involution π.

Physics

In physics, the laws of motion of classical mechanics have the above property, if the operator π reverses the conjugate momenta of all the particles of the system, " p -> -p ". (T-symmetry).

In quantum mechanical systems, it turns out that the weak nuclear force is not invariant under T-symmetry alone. If weak interactions are present, reversible dynamics are still possible, but only if the operator π also reverses the signs of all the charges, and the parity of the spatial co-ordinates (C-symmetry and P-symmetry).

Stochastic processes

A stochastic process is reversible if the statistical properties of the process are the same as the statistical properties for time-reversed data from the same process. More formally, for all sets of time increments "{ τs }", where "s = 1..k" for any k, the joint probabilities

:p(x_t, x_{t+ au_{1, x_{t+ au_{2 .. x_{t+ au_{k) = p(x_{t'}, x_{t'- au_{1, x_{t'- au_{2 .. x_{t'- au_{k)

A simple consequence for Markov processes is that they can only be reversible if their stationary distributions have the property:p(x_t=i,x_{t+1}=j) = ,p(x_t=j,x_{t+1}=i) This is called the property of detailed balance.

See also

* Reversible process
* Reversible computing


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