PhysicsNeMo Examples Catalog

Explore our comprehensive collection of examples organized by topic. Use the filters below to find examples that match your interests.

All CFD Weather/Climate Healthcare Manufacturing Molecular Dynamics Structures Transient Physics-Losses Introductory Large-scale Inference GNNs Diffusion Neural Operators Geophysics Design Optimization Symbolic Abstractions Electromagnetics

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2D Darcy Flow using Fourier Neural Operators

How to set up a data-driven model for a 2D Darcy flow using the Fourier Neural Operator (FNO) architecture inside of PhysicsNeMo.

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2D Darcy Flow using Fourier Neural Operators and Physics Losses

This example demonstrates physics informing of a data-driven model using two approaches.

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Lid Driven Cavity Flow using PINNs

Purely physics-driven model for solving a Lid Driven Cavity (LDC) flow using PINNs.

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MeshGraphNet for transient vortex shedding

This example is a re-implementation of the DeepMind's vortex shedding example deepmind/deepmind-research in PyTorch.

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Adaptive Fourier Neural Operator (AFNO) for weather forecasting

Code used for FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

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MeshGraphNet with Lagrangian mesh

Example of MeshGraphNet for particle-based simulation, based on the Learning to Simulate work.

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Learning the flow field of Stokes flow

MeshGraphNet model to learn the flow field of Stokes flow and further improve the accuary of the model predictions by physics-informed inference

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Virtual Foundary GraphNet

Graph-based deep learning approach to predict the part deformation

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AeroGraphNet for external aerodynamic evaluation

How to train the AeroGraphNet model for external aerodynamic analysis of both simplified (Ahmed body-type) and more realistic (DrivAerNet dataset) car geometries.

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Decomposable Multi-scale Iterative Neural Operator for External Aerodynamics

DoMINO is a local, multi-scale, point-cloud based model architecture to model large-scale physics problems such as external aerodynamics.

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FIGConvUNet for External Aerodynamics

FIGConvUNet is a novel architecture that can efficiently solve CFD problems for large 3D meshes and arbitrary input and output geometries.

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XAeroNet for External Aerodynamics

XAeroNet is a collection of scalable models for large-scale external aerodynamic evaluations. It consists of two models, XAeroNet-S and XAeroNet-V for surface and volume predictions, respectively.

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RNN for transient 2D Navier Stokes flow

This example uses recurrent neural networks for spatio-temporal prediction of the Navier Stokes flow.

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RNN for transient 3D Gray Scott system

This example uses recurrent neural networks for spatio-temporal prediction of the Gray-Scott reaction diffusion system.

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Nested Fourier Neural Operater for Darcy Flow

This example demonstrates how to set up a data-driven model for a 2D Darcy flow using the Nested Fourier Neural Operator (FNO).

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Shallow Water Equations - Distributed GraphCast

This example demonstrates how to leverage a distributed version of GraphCast to scale to larger Graph Neural Network (GNN) workloads.

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Temporal attention model in Mesh-Reduced space for transient vortex shedding

This example is an implementation of the paper “Predicting Physics in Mesh-reduced Space with Temporal Attention” in PyTorch.

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Transolver for Darcy Flow

This example demonstrates how to set up a data-driven model for a 2D Darcy flow using the Transolver inside of PhysicsNeMo.

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Diffusion-based-Fluid-Super-resolution

PyTorch implementation of "A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction".

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Thermal and airflow surrogate model for Datacenter design

Demonstrates the use of a Deep Learning model (3D UNet) for training a surrogate model for datacenter airflow to enable real-time datacenter design.

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MeshGraphNet for Reduced-Order cardiovascular simulations

Implements the one-dimensional Reduced-Order model based on MeshGraphNet presented in the paper Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks.

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Fourier Neural Operator for Brain anomaly detection

Fourier Neural Operator (FNO) for medical imaging.

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Molecular Dynamics using GNNs

MeshGraphNet model in PhysicsNeMo for developing a DL model for predicting forces/potential for a Lennard Jones System.

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ERA5 Data Downloader and Converter

Tools for downloading ERA5 datasets via the Climate Data Store (CDS) API and processing them into formats suitable for machine learning

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GraphCast for weather forecasting

A re-implementation of the DeepMind’s GraphCast model in PhysicsNeMo.

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DLWP model for weather forecasting

A re-implementation of the DLWP Cubed-sphere model.

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DLWP-HEALPIX model for weather forecasting

A re-implementation of the DLWP HEALPix model.

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Diagnostic models in PhysicsNeMo (precipitation)

This example contains code for training diagnostic models (models predicting an additional variable from the atmospheric state) using PhysicsNeMo.

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Unified Recipe for Training Global Weather Forecasting Models

This example demonstrates how to train a neural global weather forecast model.

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Generative Correction Diffusion Model (CorrDiff) for Km-scale Atmospheric Downscaling

A cost-effective stochastic downscaling model, CorrDiff, is trained using high-resolution weather data and coarser ERA5 reanalysis.

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StormCast: Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling

This example demonstrates how to run training and simple inference for StormCast, a generative diffusion model designed to emulate NOAA’s High-Resolution Rapid Refresh (HRRR) model, a 3km operational CAM.

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Temporal interpolation of weather forecasts

This example demonstrates how to train a deterministic ModAFNO-based model to improve the temporal resolution of weather forecasts from their native resolution of 6 hours to 1 hour.

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Diffusion Model for Full Waveform Inversion

This example demonstrates the use of diffusion models and posterior sampling for Full Waveform Inversion (FWI) in the context of geophysics.

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Inverse Problem: Recovering unknown PDE coefficients from data

Inverse PINN that recovers unknown advection-diffusion coefficients (kinematic viscosity and thermal diffusivity) from OpenFOAM observations of a heat-sink configuration. Demonstrates PhysicsInformer's detach_names mechanism and the choice between scalar and field parameterizations of the inverse variables.

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TopoDiff for performance-aware and manufacturability-aware topology optimization

This external contributiom from MIT presents a generative diffusion model with a guidance strategy to actively favors structures with low compliance and good manufacturability.

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HydroGraphNet for interpretable flood forecasting

This example demonstrates how HydroGraphNet, a physics-informed graph neural network, can be used to deliver accurate and explainable predictions of water depth and volume during flooding events.

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Automotive Crash Dynamics

This example demonstrates how machine learning surrogates can be used to access automotive crashworthiness.

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Explore Examples on GitHub

Find more examples and contribute to our open-source projects on GitHub.

• PhysicsNeMo Examples

• PhysicsNeMo Curator Examples

• PhysicsNeMo CFD Examples

• Earth2Studio Examples