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  • Displaying 13 results
    NVIDIA
    PhysicsNeMo
    NVIDIA PhysicsNeMo is an open-source framework for building, training, and fine-tuning Physics-ML models.
    Container
    NVIDIA
    PhysicsNeMo Secured Feature Branch
    NVIDIA PhysicsNeMo is an open-source framework for building, training, and fine-tuning Physics-ML models. 
    Container
    NVIDIA
    PhysicsNeMo Datasets: Stokes Flow
    Stokes flow dataset with parameterized domain
    Resource
    NVIDIA
    NVIDIA PhysicsNeMo Datacenter CFD Dataset
    Dataset and reference OpenFOAM setup for Datacenter CFD surrogate model training using PhysicsNeMo.
    Resource
    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 Ahmed Body Dataset
    This is a simulated dataset generated using OpenFOAM. The dataset comprises flow fields such as pressure and wall-shear-stress for different Ahmed body designs and inlet flow speeds.
    Resource
    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
    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
    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
    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
    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 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
    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