Pages that link to "Item:Q2225345"

The following pages link to A data-driven non-linear assimilation framework with neural networks (Q2225345):

Displaying 8 items.

• Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques (Q783088) (← links)
• Forward modeling with forced neural networks for gravity anomaly profile (Q939065) (← links)
• Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systems (Q1691147) (← links)
• Machine learning techniques to construct patched analog ensembles for data assimilation (Q2132612) (← links)
• Model and data reduction for data assimilation: particle filters employing projected forecasts and data with application to a shallow water model (Q2147287) (← links)
• “FORCE” learning in recurrent neural networks as data assimilation (Q4563874) (← links)
• Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark (Q6074252) (← links)
• Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems (Q6160016) (← links)