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06/08/2023
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
There is growing interest in discovering interpretable, closed-form equa...
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04/14/2023
Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution
Long-term stability is a critical property for deep learning-based data-...
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06/09/2022
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems
Data assimilation (DA) is a key component of many forecasting models in ...
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06/07/2022
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow
Transfer learning (TL) is becoming a powerful tool in scientific applica...
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05/09/2022
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence
Recent years have seen a surge in interest in building deep learning-bas...
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02/22/2022
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet, short for Fourier Forecasting Neural Network, is a global d...
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10/01/2021
Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation
Models used for many important engineering and natural systems are imper...
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03/16/2021
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
There is growing interest in data-driven weather prediction (DDWP), for ...
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02/25/2020
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning
To make weather/climate modeling computationally affordable, small-scale...
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07/26/2019
Analog forecasting of extreme-causing weather patterns using deep learning
Numerical weather prediction (NWP) models require ever-growing computing...
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06/20/2019
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM
In this paper, the performance of three deep learning methods for predic...
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11/12/2018
