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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 16 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 Developer Program
NVIDIA
NVIDIA
DoMINO-Automotive-Aero NIM
This NIM serves as a demonstration of the potential of foundation models for early stage design evaluation in automotive aerodynamics.
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
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
NVIDIA
Earth-2 FourCastNet 3
FourCastNet 3 is a probabilistic global weather modeling that uses geometric machine learning.
Model
NVIDIA AI Enterprise
NVIDIA
NVIDIA
NVIDIA PhysicsNeMo Datacenter CFD Dataset
Dataset and reference OpenFOAM setup for Datacenter CFD surrogate model training using PhysicsNeMo.
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
PhysicsNeMo Datasets: Stokes Flow
Stokes flow dataset with parameterized domain
Resource
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: 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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