PhysicsNeMo Examples Catalog#

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

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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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1D Wave Equation (using only Physics)

This example, walks through the process of setting up a custom PDE in PhysicsNeMo Sym.

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2D Seismic Wave Propagation (using only Physics)

This example, extends the 1D wave equation example and solve a 2D seismic wave propagation problem commonly used in seismic surveying.

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Coupled Spring Mass ODE System (using only Physics)

This example, solve a system of coupled ordinary differential equations.

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Turbulent physics: Zero Equation Turbulence Model (using only Physics)

This example, walks through the process of adding a algebraic (zero equation) turbulence model to the PhysicsNeMo Sym simulations.

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Scalar Transport: 2D Advection Diffusion (using only Physics)

This example, solves an advection-diffusion transport equation for temperature along with the Continuity and Navier-Stokes equation to model the heat transfer in a 2D flow.

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Linear Elasticity (using only Physics)

This exampleillustrates the linear elasticity implementation in PhysicsNeMo Sym.

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Inverse Problem: Finding Unknown Coefficients of a PDE

Use PhysicsNeMo Sym to solve an inverse problem by assimilating data from observations.

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2D Darcy Flow using Fourier Neural Operators (via Symbolic Abstractions)

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

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2D Darcy Flow using Adaptive Fourier Neural Operators (via Symbolic Abstractions)

PhysicsNeMo Sym to set up a data-driven model for a 2D Darcy flow using the Adaptive Fourier Neural Operator (AFNO).

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2D Darcy Flow using Physics Informed Fourier Neural Operators (via Symbolic Abstractions)

PhysicsNeMo Sym to set up a data+physics model for a 2D Darcy flow using the Fourier Neural Operator (FNO).

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Deep Operator Networks (via Symbolic Abstractions)

Using DeepONets in PhysicsNeMo Sym

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Interface Problem by Variational Method (using only Physics)

This example demonstrates the process of solving a PDE using the variational formulation.

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Blood Flow in Intracranial Aneurysm (using only Physics)

This example demonstrates how to train the PINNs to predict flow in a complex geometry imported as STL.

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Moving Time Window: Taylor Green Vortex Decay (using only Physics)

This example presents a moving time window approach to solve a complex transient Navier-Stokes problem.

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Electromagnetics: Frequency Domain Maxwell’s Equation (using only Physics)

This example demonstrates how to use PhysicsNeMo Sym to do the electromagnetic (EM) simulation.

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Fully Developed Turbulent Channel Flow (using only Physics)

This example demonstrates the use of PINNs to solve a canonical turbulent flow in a 2D channel using two equation turbulence models and wall functions, without using any training data.

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Turbulence Super Resolution (via Symbolic Abstractions)

This example uses PhysicsNeMo Sym to train a super-resolution surrogate model for predicting high-fidelity homogeneous isotropic turbulence fields from filtered low-resolution observations provided by the Johns Hopkins Turbulence Database.

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Conjugate Heat Transfer (using only Physics)

This example uses PhysicsNeMo Sym to study the conjugate heat transfer between the heat sink and the surrounding fluid.

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Parameterized 3D Heat Sink (using only Physics)

This example walks through the process of simulating a parameterized problem using PhysicsNeMo Sym.

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Heat Transfer with High Thermal Conductivity (using only Physics)

This example discusses strategies that can be employed for handling conjugate heat transfer problems with higher thermal conductivities that represent more realistic materials.

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FPGA Heat Sink with Laminar Flow (using only Physics)

This example shows how some of the features in PhysicsNeMo Sym apply for a complicated FPGA heat sink design and solve the conjugate heat transfer.

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Industrial Heat Sink CFD (using only Physics)

This example uses PhysicsNeMo Sym to conduct a thermal simulation of NVIDIA's NVSwitch heatsink.

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Explore Examples on GitHub#

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