  We demonstrate that the Maximum Lyapunov Exponent for computable dynamical
systems is isomorphic to the maximum capacity of a noiseless, memoryless
channel in a Shannon communication model. The isomorphism allows the
understanding of Lyapunov exponents in the simplified terms of Information
Theory, rather than the traditional definitions in Chaos Theory. This work
provides a bridge between fundamental physics and Information Theory to the
mutual benefit of both fields. The result suggests, among other implications,
that machine learning and other information theory methods can be successfully
employed at the core of physics simulations.
