Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems

While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and their applicability over macroscopic time scales of physical interest is computationally infeasible or impractical. In complex systems, with limited physical insight on the coherent behavior of their constituents, the only available information is data obtained from simulations of the trajectories of huge numbers of degrees of freedom over microscopic time scales. The analysis of these large amounts of data hinges upon the ability to efficiently extract meaningful latent properties and to discover reduced, predictive descriptions. This paper discusses a Bayesian approach to deriving probabilistic coarse-grained models that simultaneously address the problems of identifying appropriate reduced coordinates and the effective dynamics in this lower-dimensional representation. At the core of the models proposed lie s...

• Type: Journal
• Year: 2011
• Where: MMAS
• Authors: Phaedon-Stelios Koutsourelakis, Elias Bilionis
• Keywords: Building Blocks, Intelligent Agents, Macroscopic Time Scales, MMAS 2011, Time Scales