Thermal Modeling with PINNs: Bringing Physics-Based Simulation to Real-Time and Edge Applications

Thermal modeling is critical across engineering systems—from battery packs and power electronics to automotive, aerospace, energy storage and industrial equipment. Today, FEM, FVM and CFD remain the backbone of high-fidelity thermal simulation. They are trusted, well understood and essential for design and validation.

But engineering teams are now facing two related challenges.

The first is speed. Thermal design often requires multiple iterations and parametric studies across geometry, materials, boundary conditions, cooling strategies, duty cycles and operating environments. Running these studies through conventional high-fidelity simulation can become time-consuming when teams need to explore large design spaces quickly.

The second is deployment. How do we take validated physics models beyond the simulation environment and use them for faster what-if studies, near real-time prediction, field diagnostics or even edge deployment?

This webinar explores the role of Physics-Informed Neural Networks (PINNs) in thermal modeling and how they can complement existing simulation workflows by making physics-based models faster, lighter and easier to deploy.

We will discuss how PINNs combine governing equations, boundary conditions, simulation data and experimental measurements to build fast, physics-consistent surrogate models for temperature prediction, thermal reconstruction, inverse heat-transfer problems, digital twins and near real-time thermal intelligence.

The session will also address the practical considerations: where PINNs work well, where caution is needed, and how to approach validation, training effort, extrapolation risk, boundary conditions and accuracy compared with conventional simulation.

For teams working across batteries, mobility, aerospace, energy and industrial systems, this webinar offers a practical perspective on moving thermal modeling from offline simulation to deployable engineering intelligence.