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Status: Unchecked ×Keyword: high-dimensional systems ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Model Reduction and Neural Networks

   CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation

   Chen, Chen and Wu · Computer Physics Communications · 2024

   The paper develops CGNSDE, a hybrid of knowledge-based modelling and neural networks, for predicting complex systems and estimating their hidden states, and tests it on chaotic systems.

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   1. UncheckedIn numerical tests on chaotic systems, the CGNSDE model beat knowledge-based regression models, and adding the data assimilation loss improved it further.“Numerical experiments based on chaotic systems with intermittency and strong non-Gaussian features indicate that the CGNSDE outperforms knowledge-based regression models, and the DA loss further enhances the modeling skills of the CGNSDE.”
   2. UncheckedTraining the CGNSDE model with an extra data assimilation loss makes it better at estimating extreme events and their uncertainty, the authors state.“With the DA loss, the CGNSDE is more capable of estimating extreme events and quantifying the associated uncertainty.”