Computational Science Laboratory
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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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Scholarly Works, Computational Science Laboratory [3]
Research articles, presentations, and other scholarship
Recent Submissions
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Robust data assimilation using L1 and Huber norms
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
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
(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 ...
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