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Found 3 papers, 0 papers with code
An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation
Since HOOPE-PF is the extension of the existing particle filter which has been extensively applied to many earth system models such as land, ecosystem, hydrology, and paleoclimate reconstruction, the HOOPE-PF can be applied to improve the simulation of these earth system models by considering time-varying model parameters.
Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models
Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations.
Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model
Second, I developed the statistical machine learning based surrogate model, which is computationally cheap but accurately mimics the relationship between parameters and the cost function, by applying the Gaussian process regression to learn the model simulation.
