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EPSRC Reference: GR/R83347/01
Title: Algorithms for Extracting Effective Macroscopic dynamics
Principal Investigator: Stuart, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Mathematics
Organisation: University of Warwick
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 2002 Ends: 30 September 2005 Value (£): 143,437
EPSRC Research Topic Classifications:
Mathematical Analysis Numerical Analysis
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
12 Dec 2001 Mathematics Responsive Dec 01 Deferred
Summary on Grant Application Form
research in about 200 words.The idea that complex micro-descriptions of physical phenomena can have simpler macro-descriptions is central to the theory of statistical physics and is a well-established notion. Finding algorithms which will carry out this reduction on certain classes of problems is a relatively new idea. This proposal is concerned with the study of such algorithms in the situation where the simpler macro-model, to be determined algorithmically, is stochastic, even if the underlying micro-model is not.The algorithms we study were initially developed for simple model dynamical systems, were then applied to models of biomolecular conformations, and are now being investigated in a wide range of application areas such as climatology, Their behaviour on complex problems is promising, but not well understood. This proposal is concerned with the development of tools to understand, and improve, the algorithms.Summary Key Words: Numerciai Analysis, Dynamical Systems, Random Data, Effective Stochastic Dynamics.
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Organisation Website: http://www.warwick.ac.uk