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Showing 1 to 4 of 4 for “"Kalman Smoothing"”.

  1. Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing

    … this in two steps: a derivative estimation and smoothing step, followed by sparse regression over a library of candidate functions. Previous implementations of the derivative step in SINDy, including the Python package pysindy, relied on methods such as finite difference, L1 total variation …

    washington Repository record for Sparse Learning of Nonlinear PDE Dynamics using Kalman Smoothing (opens in a new tab)

  2. A state-space approach to dynamic tomography

    … of the convergence of the ensemble Kalman filter, a new method for ensemble Kalman smoothing and theory regarding its convergence, the first four-dimensional reconstruction of electron density in the solar atmosphere, a new method for dynamic tomography called the Kalman-Wiener …

    uiuc Repository record for A state-space approach to dynamic tomography (opens in a new tab)

  3. Model-based spectral inference in noisy physical time series: applications in laser linewidth estimation and precision magnetometry

    … from 3He mag- netometry, employing an extended Kalman smoothing approach with Expectation – Maximization-based automatic tuning of model parameters. These methods illustrate how incorporating physical system knowledge into sta- tistical inference enhances the accuracy and robustness of spectral …

    tu-berlin Repository record for Model-based spectral inference in noisy physical time series: applications in laser linewidth estimation and precision magnetometry (opens in a new tab)

  4. Improved Gaussian process approximations for spatial and flow fields

    … - approximating the state posterior with Kalman smoothing - leading to faster and less biased learning, while also applicable to either discrete or continuous time. Overall, this thesis presents improved GP variational approximations to make learning cheaper, with applicability to a …

    cambridge Repository record for Improved Gaussian process approximations for spatial and flow fields (opens in a new tab)