Colin Grudzien

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Research interests

Chaotic dynamics are often described in terms of the “butterfly effect” where a minor change in the initialization of a model, tantamount to the flap of a butterfly wing, produces a radically different outcome over the course of a model run. My research interests lie primarily in the intersection of data, stability and prediction, to understand how dynamic instability impacts the uncertainty of predictive models. My postdoctoral appointment at NERSC is funded by the Reduced subspace in big data treatment: A new paradigm for efficient geophysical Data Assimilation (REDDA) project.

Recently Submitted:

  • C. Grudzien, A. Carrassi and M Bocquet 2018. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error – Preprint
  • C. Grudzien, A. Carrassi and M. Bocquet 2017. Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error. – Preprint
  • C. Grudzien, D. Deka, M. Chertkov, S.N. Backhaus 2017. Structure- & physics- preserving reductions of power grid models. – Preprint

Please see my Github site below for preprints of other works, code and data visualizations.  My public key is linked here.

Curriculum Vitae