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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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06/06/2023
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Fourier Neural Operators (FNOs) have proven to be an efficient and effec...
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10/21/2022
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
Data-driven models, such as FourCastNet (FCN), have shown exemplary perf...
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08/08/2022
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly deva...
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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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02/15/2021
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
We consider the problem of data-assisted forecasting of chaotic dynamica...
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09/30/2020
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Simulation of turbulent flows at high Reynolds number is a computational...
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02/10/2020
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
We consider the commonly encountered situation (e.g., in weather forecas...
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12/05/2019
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
We introduce and test a general machine-learning-based technique for the...
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10/09/2019
Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms
How effective are Recurrent Neural Networks (RNNs) in forecasting the sp...
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03/09/2018
