Data-driven stochastic model reduction (Fei Lu - University of California Berkeley)

The need to infer reduced computational models of complex systems from discrete partial observations arises in many scientific and engineering applications, for example in climate prediction, materials science, and biology. The challenges come mainly from memory effects due to unresolved scales, from nonlinear interactions between resolved and unresolved scales, and from the difficulty in drawing inferences from discrete partial data. We address these challenges by a discrete-time stochastic parametrization method, and demonstrate by examples that the resulting stochastic reduced models can capture the key statistical dynamical features of the full system and make accurate short-term predictions. The examples include the Lorenz 96 system (which is a simplified model of the atmosphere) and the Kuramoto-Sivashinsky equation that describes spatiotemporally chaotic dynamics.