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Found 5 papers, 0 papers with code
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction.
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations.
High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics.
Feature Importance in a Deep Learning Climate Emulator
From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider.
Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models
We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales.
