Introduction:
| |
Effective sampling is the key element for successfully simulating the properties and behavior of complex systems. Large sizes, slow processes, and sophisticated structures typical for such systems make achieving efficient sampling of their possible realizations a challenging task.
Faster computer technologies, allowing for longer length simulations, greatly contributed to recent progress in understanding complex systems and processes. However, they alone are not sufficient to tackle these great computational challenges.
Recently, many interesting approaches have been developed in an attempt to improve the sampling techniques in complex systems simulation. These include Monte Carlo approaches (e.g. simulated annealing, importance sampling, hybrid Monte Carlo, parallel tempering), harmonic approximations, coarse-graining formulations, multi-scale integrators, transition path sampling, Markov Chain models, new parallel algorithms, and specialized hardware systems.
The most successful methods rely on adaptation of novel mathematical ideas to specific applications, on latest developments in computer sciences and engineering innovations. It has become clear that any future progress in the study of complex systems and processes is impossible without the use of novel multidisciplinary approaches, which allow for enhanced sampling and transcend the current limits on imposed spatial and temporal scales.
The purpose of this session is to bring together experts in mathematics, computer and natural sciences, statistics, and engineering for exchanging experiences and ideas, in order to combine efforts in developing new methodologies for efficient simulation of complex systems. |
|