The mission of the Computational Science Laboratory (CSL) is to develop innovative computational solutions for complex real-world problems, and to foster a productive research and education environment emphasizing collaboration and innovation.

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Recent Submissions

  • Robust data assimilation using L1 and Huber norms 

    Rao, Vishwas; Sandu, Adrian; Ng, Michael; Nino-Ruiz, Elias
    Data assimilation is the process to fuse information from priors, observations of nature, and numerical models, in order to obtain best estimates of the parameters or state of a physical system of interest. Presence of ...
  • Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques 

    Moosavi, Azam; Stefanescu, Razvan; Sandu, Adrian
    Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems. This paper applies machine learning ...
  • The reduced-order hybrid Monte Carlo sampling smoother 

    Attia, Amed; Stefanescu, Razvan; Sandu, Adrian (Wiley-Blackwell, 2017-01-10)
    Hybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulated in the Bayesian framework. The smoother ...