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Displaying 19 results
NVIDIA Earth-2 Inference
NVIDIA
NVIDIA
Earth-2 FourCastNet
FourCastNet predicts global atmospheric dynamics of various weather / climate variables.
Container
NVIDIA AI Enterprise
NVIDIA
NVIDIA
Earth-2 FourCastNet SFNO
FourCastNet V2 model for predicting atmospheric dynamics.
Model
NVIDIA Earth-2 Inference
NVIDIA
NVIDIA
Earth-2 CorrDiff
Correction Diffusion (CorrDiff) is a generative AI model that downscales surface and atmospheric variables to improve the accuracy and resolution of weather data.
Container
NVIDIA AI Enterprise
NVIDIA
NVIDIA
Earth-2 CorrDiff US
Corrector Diffusion (CorrDiff) US GEFS-HRRR is a generative downscaling model for the contiguous United States.
Model
NVIDIA
NVIDIA
Earth-2 FourCastNet 3
FourCastNet 3 is a probabilistic global weather modeling that uses geometric machine learning.
Model
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
TFT PyT checkpoint (Base, AMP, Electricity)
TFT Base PyTorch checkpoint trained with AMP on Electricity dataset
Model
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
TFT PyT checkpoint (Base, AMP, Electricity)
TFT Base PyTorch checkpoint trained with AMP on Electricity dataset
Model
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
TFT PyT checkpoint (Base, AMP, Traffic)
TFT Base PyTorch checkpoint trained with AMP on Traffic dataset
Model
NVIDIA
NVIDIA
NVIDIA Time Series Prediction Platform
NVIDIA Time Series Prediction Platform is a tool designed to compare easily and experiment with arbitrary combinations of forecasting models, time-series datasets, and other configurations.
Resource
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
TFT PyT checkpoint (Base, AMP, Traffic)
TFT Base PyTorch checkpoint trained with AMP on Traffic dataset
Model
NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
Temporal Fusion Transformer for PyTorch
Temporal Fusion Transformer is a state-of-the-art architecture for interpretable, multi-horizon time-series prediction.
Resource
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5
This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), interpolates a prognostic forecast model to a shorter time-step size (by default from 6 h to 1 h).
Model
NVIDIA
NVIDIA
NVIDIA PhysicsNeMo Sym Examples Supplemental Materials
This contains all the supplemental data for PhysicsNeMo Sym examples. This includes validation data, training data, large CSV/STLs, etc. For the python scripts, please refer https://github.com/nvidia/physicsnemo-sym
Resource
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5
This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour accumulated surface precipitation given 20 atmospheric variables plus invariants.
Model
NVIDIA
NVIDIA
NVIDIA PhysicsNeMo Examples Supplemental Materials
This contains all the supplemental data for PhysicsNeMo examples. This includes files apart from the actual dataset required for training. For the python scripts, please refer https://github.com/nvidia/physicsnemo
Resource
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5
This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour accumulated surface solar irradiance given 24 atmospheric variables plus invariants.
Model
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: DLESyM-V1-ERA5
DLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling, including atmosphere and ocean components. The model operates with 6 hour temporal resolution, forecasting 8 atmospheric variables and 1 oceanic variable.
Model
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: StormCast-V1-ERA5-HRRR
StormCast-V1-ERA5-HRRR is a mesoscale machine learning AI model that autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmosphere boundary layer.
Model
NVIDIA
NVIDIA
PhysicsNeMo Checkpoints: AFNO_DX_WG-V1-ERA5
This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), predicts 6-hour maximum 3-second wind gusts given 20 atmospheric variables plus invariants.
Model

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