  Thermodynamics is based on the notions of energy and entropy. While energy is
the elementary quantity governing physical dynamics, entropy is the fundamental
concept in information theory. In this work, starting from first principles, we
give a detailed didactic account on the relations between energy and entropy
and thus physics and information theory. We show that thermodynamic process
inequalities, like the Second Law, are equivalent to the requirement that an
effective description for physical dynamics is strongly relaxing. From the
perspective of information theory, strongly relaxing dynamics govern the
irreversible convergence of a statistical ensemble towards the maximally
non-commital probability distribution that is compatible with thermodynamic
equilibrium parameters. In particular, Markov processes that converge to a
thermodynamic equilibrium state are strongly relaxing. Our framework
generalizes previous results to arbitrary open and driven systems, yielding
novel thermodynamic bounds for idealized and real processes.
