Find the problem that looks like yours
Train once, then ask what you could not afford to ask
Try to beat STOCHOS on an engineering problem
You get a few tries to shape a plastic bottle that passes a 26 bar pressure test at the lowest weight and cost. Then STOCHOS takes the same problem, on the same score, and you see both answers side by side.
About 2 minutes · illustrative demo, synthetic data, response surfaces fitted offline from DIM-GP models
Engineering teams, on the record
Named cases and third-party benchmarks, in trade press and at NAFEMS. Each links to the full story.
Bring one engineering bottleneck
Start with one workflow. We outline where a model fits, which outputs to predict, how validation works, and what a realistic proof of concept looks like.
Runs on your infrastructure · supports air-gapped deployment via an offline license
Common questions
01How can AI accelerate CFD and FEM simulations?
STOCHOS learns a surrogate model from existing solver data, then predicts results for new parameters or geometries in seconds. Fast, uncertainty-aware predictions let you explore many more variants than the solver alone could cover, and the full simulation returns only for the designs you shortlist.
02Can STOCHOS predict full fields as well as single values?
Yes. STOCHOS handles 2D and 3D fields from CFD and FEM, as well as scalars, signals, meshes, and geometries. It can predict spatially resolved outputs for new parameter combinations, so you see more than a single KPI when comparing designs. Confidence is attached to every prediction, field or scalar.
03How does STOCHOS work with Ansys?
PI Probaligence is an official Ansys Technology Partner. STOCHOS Flow reads Ansys Workbench parameters and calls the Ansys Solver nodes for selective validation, so fast surrogate prediction connects to the solver workflows engineering teams already use. New solver results feed back into training, and the surrogate improves inside the Workbench setup your team runs today.
04How much data does STOCHOS need to be useful?
Less than most machine-learning tools, but there is no fixed number. DIM-GP is designed for small, expensive datasets, so teams usually start from existing solver runs. How many you need depends on the physics, the number of parameters, and the outputs. Quantified uncertainty then shows where the model is reliable and where more runs are worth adding.
