Continuous-time Markov processes describe the evolution of probability
(or ensemble) distributions on systems with constant transition rates
between microscopic states. In this setting we explore the use of
balanced truncation, a model reduction technique from robust control
theory, for deriving macroscopic models of system dynamics. In
particular, we aim to find free energies between macroscopic states and
to identify important transition states. Applications include DNA
secondary structure dynamics and tiling self-assembly
processes.
