Simulate a Heat Shield with PhysicsNeMo
Use AI to predict how heat flows through a spacecraft's thermal protection
Last reviewed: March 2026Overview
When spacecraft re-enter Earth's atmosphere, their heat shields reach temperatures over 1,600°C. Engineers need to predict exactly how heat flows through the shield material to keep astronauts safe. Traditionally this requires expensive computer simulations — but physics-informed neural networks (PINNs) offer a faster alternative.
In this project you'll use NVIDIA PhysicsNeMo, an open-source framework for physics-ML, to solve the steady-state heat equation on a simple 2D plate geometry. Instead of dividing space into a mesh (like traditional methods), PhysicsNeMo trains a neural network to learn the temperature field by enforcing the physics equations as a loss function.
You'll see how a neural network can "learn" physics without any simulation data — just the equations themselves — and produce a smooth temperature map that you can query at any point. This is the same technology NASA and SpaceX engineers are exploring for rapid thermal analysis.
What You'll Learn
- ✓ Understand the steady-state heat equation and what boundary conditions mean physically
- ✓ Set up and run a PhysicsNeMo PINN training workflow
- ✓ Visualize neural network predictions as temperature contour plots
- ✓ Compare PINN results to analytical solutions for simple geometries
- ✓ Explain why physics-informed ML is useful for engineering problems
Step-by-Step Guide
Install PhysicsNeMo
Install NVIDIA PhysicsNeMo following the official docs. You'll need Python 3.8+ and PyTorch. If you don't have an NVIDIA GPU, you can run on CPU (slower) or use Google Colab with a free GPU. Verify installation by running one of the built-in examples.
Define the Heat Problem
Set up a 2D rectangular plate with fixed temperatures on the edges — for example, 500°C on the left (the hot side facing re-entry plasma) and 20°C on the right (the cool side facing the cabin). The heat equation says: ∇²T = 0 for steady state. Define this geometry and these boundary conditions in PhysicsNeMo.
Train the PINN
PhysicsNeMo samples random points inside your domain and on the boundaries, then trains a neural network to minimize two losses: how well it satisfies the heat equation at interior points, and how well it matches boundary temperatures. Run training for a few thousand iterations and watch the loss decrease.
Visualize the Temperature Field
Query the trained network on a dense grid of points and plot the predicted temperature as a contour map using matplotlib. You should see a smooth gradient from hot to cold. Add contour lines to show isotherms (lines of equal temperature).
Compare to the Exact Solution
For a simple rectangular plate with these boundary conditions, the exact solution is a linear temperature gradient. Calculate the analytical answer and compare it to your PINN prediction. How close are they? Plot the error map.
Try a More Complex Shape
Modify the geometry — add a notch, hole, or curved edge to simulate a more realistic heat shield cross-section. The analytical solution no longer exists, but the PINN handles it just as easily. Visualize how the temperature field changes around geometric features.
Career Connection
See how this project connects to real aerospace careers.
Aerospace Engineer →
Thermal protection system design is critical for re-entry vehicles — engineers must predict heat flow through shield materials
Space Operations →
Understanding thermal constraints helps mission planners schedule operations to avoid overheating spacecraft components
Aerospace Manufacturing →
Manufacturing heat shield tiles requires understanding thermal gradients to prevent cracking during fabrication
Go Further
Take your thermal analysis further:
- Time-dependent heat — solve the transient heat equation to see how temperature changes over time during re-entry
- Multiple materials — model a heat shield with layers of different thermal conductivity
- 3D geometry — extend to a 3D heat shield nose cone shape
- Compare to OpenFOAM — run the same problem in OpenFOAM and compare accuracy and speed