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  • SearchSearch thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
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  • Displaying 13 results
    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
    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
    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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