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Predictability in Spatially Extended Systems with Model Uncertainty

2008/12/18 by Jinqiao Duan, Duan, Jinqiao
Decision Sciences · Earth and Planetary Sciences · Environmental Science · Mathematics · #35R60 #37H10 #60H15 #60H30 #Analysis of PDEs (math.AP) #Climate variability and models #Dynamical Systems (math.DS) #FOS: Mathematics #Meteorological Phenomena and Simulations #Probabilistic and Robust Engineering Design #Probability (math.PR) #math.AP #math.DS #math.PR #msc:35R60 #msc:37H10 #msc:60H15 #msc:60H30

paper · pdf · doi:10.48550/arxiv.0812.3679

To appear in the Journal "Engineering Simulation", 2009

openalex publication_date 2008/12/18 · arxiv created 2009/03/26 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Macroscopic models for spatially extended systems under random influences are often described by stochastic partial differential equations (SPDEs). Some techniques for understanding solutions of such equations, such as estimating correlations, Liapunov exponents and impact of noises, are discussed. They are relevant for understanding predictability in spatially extended systems with model uncertainty, for example, in physics, geophysics and biological sciences. The presentation is for a wide audience.

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