Monte Carlo (MC) simulations are an indispensable tool for the investigation of physical models. The most efficient MC weights for the calculation of physical, canonical expectation values are not necessarily those of the canonical ensemble, but the use of suitably generalized ensembles can lead to much faster convergence. Although not realized by nature, these ensembles can be implemented on computers.
In recent years the generalized ensemble approach has in particular
been studied for the simulation of complex systems. For these systems
it is typical that conflicting constraints lead to free energy barriers,
which fragment the configuration space. Examples of major interest are
spin glasses and proteins. In my overview I will first comment on the strengths
and weaknesses of a few major approaches, including parallel tempering,
multicanonical and transition variable methods. Subsequently, selected
examples from applications to spin glasses and proteins will be presented.
For a review of the generalized ensemble approach in the context of protein folding see Hansmann and Okamoto . The multicanonical approach, to some extent along with parallel tempering and transition variable methods, is reviewed in Ref. . The featured applications will include Helix-coil transitions of amino-acid homo-oligomers  and a new analysis of the Parisi overlap distribution from a simulation of the 3d Edwards-Anderson Ising spin glass 
|Zeit:||Montag, 05. Nov. 01 um 14.15 Uhr, Tee/Kaffee ab 14.00 Uhr|
|Ort:||ZIB, Takustr. 7, 14195 Berlin-Dahlem|
|Raum:||Seminarraum 2006 (EG, im Rundbau)|