Multi-fidelity learning: coarse mesh results and fine mesh results both feed a DIM-GP model, which predicts the full stress field in the highest quality and reduces how much high-fidelity simulation is needed. — Coarse and fine simulations feed one model. It returns a full high-quality field, so fewer expensive high-fidelity runs are needed.

Method

What is multi-fidelity modeling?

Multi-fidelity modeling blends a few accurate, expensive data points with many cheap, rough ones. You get predictions across the whole range at lower cost, with confidence attached. STOCHOS learns how the sources relate inside one probabilistic model.

Explore STOCHOS How STOCHOS works

The idea in three plots

Cheap data covers the space. Accurate data gets it right.

Follow one example the whole way down: how an additive changes a material's impact strength. Same question, two kinds of data, one model that uses both.

Not all data costs the same

Lab tests are accurate but slow, so you run five. Simulations are rough but cheap, so you run thirty. Two views of the same curve, at very different prices.

Pick one and you lose something

Use only the accurate points and you are right where you measured, blind in between. Use only the cheap points and you cover everything but drift off the truth. Neither source is enough alone.

Two panels titled Choose One, Both Have Problems. Expensive only: an accurate fit with large gaps between points. Cheap only: a dense fit that is biased away from the dashed true-behavior curve. — Expensive only leaves gaps. Cheap only is biased. The dashed line is the truth.

Combine them in one model

A multi-fidelity model learns how the two sources relate. The few accurate points anchor the answer; the many cheap points fill the space between them. One prediction across the whole range, with a band that widens where evidence is thin.

Plot titled Multi-Fidelity, Use Everything: a DIM-GP fit that follows the true behavior curve, anchored by the expensive points and filled in by the cheap points, with an uncertainty band around it. — The combined DIM-GP fit follows the true curve; the band shows what is still uncertain.

In STOCHOS

Built into the model, not bolted on

STOCHOS learns how the fidelity levels relate inside one probabilistic model, the DIM-GP, and reports confidence everywhere. There is no separate, hand-tuned pipeline. The same approach spans coarse and fine simulation, simulation and experiment, or lab and production.

Multi-fidelity is a surrogate model that spends your data budget where uncertainty says it counts.

A concrete example: coatings

Liquid-state measurements are cheaper than dried-film tests, and lab data is cheaper than production data. A multi-fidelity model predicts final properties from both, without running every expensive test.

Next method

05 / 06

Sensitivity Analysis

Explain it: identify which inputs actually drive the outcome.

Keep reading

Where this applies

Bring your data

Your cheap and expensive data belong in one model.

Show us the sources you have: the ones you trust and the ones you can afford. We build a multi-fidelity DIM-GP on them and show where each one earns its place.

Request a Demo How STOCHOS works

Runs on your infrastructure. Your data stays with you.

Common questions

01Why not just use the accurate data?

Accurate data is usually expensive and scarce, so on its own it rarely covers the whole range. Multi-fidelity modeling adds cheap, broad data for coverage and uses the accurate data to anchor the answer. You get a better prediction at lower total cost.

02Does multi-fidelity modeling only apply to simulation?

No. The idea works wherever sources differ in cost and accuracy: coarse and fine simulation, simulation and experiment, or lab, pilot, and production. STOCHOS handles these combinations inside one model.

03Can the cheap data make the prediction worse?

Yes. If the cheap source does not track the accurate one, it can mislead the model. That is why the accurate data stays in control: it anchors the answer, and the model reports where confidence is low. You check the combined model against the accurate data alone.

04What is multi-fidelity modeling?

Multi-fidelity modeling combines cheap, lower-accuracy data, such as coarse simulations, with a few expensive, high-accuracy runs. The cheap data covers the design space, the accurate data anchors the prediction, and STOCHOS learns from both in one model, so you get reliable predictions at lower total cost.