Open Source Summit India 2026

Predicting how air flows and heat distributes through a physical product is essential for product feasibility studies, performance analysis etc. But it takes hours to days per design variant. You model the geometry, generate a mesh, set up boundary conditions, run the solver, post-process the results. The bigger issue is the back-and-forth between the designer and the simulation engineer. Multiply that by the dozens to hundreds of variants you need to explore a design space, and you get missed deadlines, stale questions, and engineering time spent waiting instead of thinking.
This talk covers usage of OpenFOAM to generate parametric training datasets across geometric and flow parameters with Latin Hypercube Sampling, then train a surrogate model using NVIDIA's PhysicsNeMo framework and its DoMINO (Deep Operator Network for Multi-physics) architecture. DoMINO encodes 3D geometry as a signed distance field, combines it with local surface features, and predicts pressure, temperature, and velocity directly on the part surface in a single forward pass, replacing the entire simulation chain. We'll go through how DoMINO works and why it fits this class of problems.