ML-based surrogate models that replace expensive FEM simulations—near real-time predictions with controlled accuracy.
A B2B reference: we train surrogates from your simulation data so design exploration, optimisation, and digital twins run in milliseconds instead of hours.
A surrogate model is a machine learning model trained on simulation results (e.g. FEM, CFD) to approximate their output. Once trained, it predicts result fields—stress, displacement, temperature—in milliseconds instead of hours.
This unlocks workflows that are infeasible with the full solver alone: large parameter sweeps, design optimisation, real-time decision support, and interactive digital twins.
Surrogates do not replace the underlying physics. They complement it: the high-fidelity solver remains the source of truth, the surrogate provides speed where it matters.
We train a neural surrogate on a dataset of FEM simulations of a structural component under varying loads and geometries. The model learns the mapping from input parameters to the full stress field. The image below shows a typical FEM result—mechanical stress magnitude on a bracket geometry—exactly the kind of output the surrogate reproduces in milliseconds.
