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Displaying 17 results

Earth-2 FourCastNet
NVIDIA
FourCastNet predicts global atmospheric dynamics of various weather / climate variables.
Container
Earth-2 CorrDiff
NVIDIA
Correction Diffusion (CorrDiff) is a generative AI model that downscales surface and atmospheric variables to improve the accuracy and resolution of weather data.
Container
Earth-2 FourCastNet SFNO
NVIDIA
FourCastNet V2 model for predicting atmospheric dynamics.
Model
DoMINO-Automotive-Aero NIM
NVIDIA
This NIM serves as a demonstration of the potential of foundation models for early stage design evaluation in automotive aerodynamics.
Container
PhysicsNeMo Secured Feature Branch
NVIDIA
NVIDIA PhysicsNeMo is an open-source framework for building, training, and fine-tuning Physics-ML models. 
Container
Earth-2 CorrDiff US
NVIDIA
Corrector Diffusion (CorrDiff) US GEFS-HRRR is a generative downscaling model for the contiguous United States.
Model
NVIDIA PhysicsNeMo Ahmed Body Dataset
NVIDIA
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
Earth-2 FourCastNet 3
NVIDIA
FourCastNet 3 is a probabilistic global weather modeling that uses geometric machine learning.
Model
NVIDIA PhysicsNeMo Datacenter CFD Dataset
NVIDIA
Dataset and reference OpenFOAM setup for Datacenter CFD surrogate model training using PhysicsNeMo.
Resource
PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5
NVIDIA
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 Sym Examples Supplemental Materials
NVIDIA
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
PhysicsNeMo Checkpoints: AFNO_DX_TP-V1-ERA5
NVIDIA
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
PhysicsNeMo Datasets: Stokes Flow
NVIDIA
Stokes flow dataset with parameterized domain
Resource
NVIDIA PhysicsNeMo Examples Supplemental Materials
NVIDIA
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
PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5
NVIDIA
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
PhysicsNeMo Checkpoints: DLESyM-V1-ERA5
NVIDIA
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
PhysicsNeMo Checkpoints: AFNO_DX_WG-V1-ERA5
NVIDIA
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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