A Step-by-Step Coding Tutorial on NVIDIA PhysicsNeMo: Darcy Flow, FNOs, PINNs, Surrogate Models, and Inference Benchmarking

MarkTechPost / 4/14/2026

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The article provides a step-by-step tutorial for implementing NVIDIA PhysicsNeMo on Colab to create a practical physics-informed ML workflow.
It walks through environment setup, data generation, and visualization for the 2D Darcy Flow problem to make the learning task concrete.
It then guides readers through implementing and training multiple model types, including FNOs, PINNs, and surrogate models, for the physics simulation task.
The tutorial concludes with an approach to inference benchmarking, helping evaluate model performance in a reproducible way.

In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start by setting up the environment, generating data for the 2D Darcy Flow problem, and visualizing the physical fields to clearly understand the learning task. From there, we implement and train powerful models such as the […]

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