01
Main view and top view of the same shape. Each frame is a geometry the model can hand straight to a solver.
The engineering loop
Train once, then ask what you could not afford to ask
01
Data you already paid for
Every finished project leaves solver runs, test-bench results and measurements behind, and none of it informs the next one.
With STOCHOS
Those runs train a surrogate model on your own physics. There is no new simulation campaign to start.
02
The questions you skip
Optimization across many parameters, sensitivity and robustness studies never run, because the solve budget goes to single designs.
With STOCHOS
Predictions arrive in seconds, each carrying how far it can be trusted, so those studies become affordable.
03
Where the solver goes
High-fidelity simulation stays essential, but it is spent on every question equally, including the ones the last project already answered.
With STOCHOS
Run the full solver where the model is unsure or where you need proof. That result feeds back into training, and the loop repeats.
In three dimensions
What the model returns on a whole body
One shape, three surfaces: the field, the model's confidence, the check.
01 Prediction
02 Confidence
03 Reality
Generated geometry
The space between your designs is a space you can search
Train on the shapes you have already built and the model learns a continuous space of geometry, not a list of variants. Every point in it is a shape it can hand straight to a solver, and the ones between your training shapes are new.
02
Blade count, wrap and hub profile move together, so the shape stays one continuous object rather than a set of edits.
03
The shell changes shape without ever leaving the set the model considers buildable.
Physics in motion
Cases that move, not one frozen state
01 Seal
02 Mixer
03 Granular bed
04 Sloshing
Interactive demo
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
01 Set·02 Run·03 Lock·04 Compare
Proof
Engineering teams, on the record
Named cases and third-party benchmarks, in trade press and at NAFEMS. Each links to the full story.
Get started
Find the problem that looks like yours
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, and there is no fixed minimum. DIM-GP is designed for small, expensive datasets, so teams usually start from existing solver runs: tens to low hundreds, not thousands. Above roughly a thousand samples, cheaper methods start to make more sense than a Gaussian process. How many you need depends on the physics, the number of parameters, and the outputs, and quantified uncertainty shows where the model is reliable and where more runs are worth adding.
