How QodeX Quantum Works
A disciplined four-step loop that turns design exploration from a slow, guessing-game queue into a deterministic, data-backed optimization search.
Step 1 — Define the design space
You tell the system what can change: geometric parameters, boundary conditions, material choices, and constraints. You set the objective function: minimize drag, minimize peak temperature, minimize mass.
Step 2 — Train fast surrogate models
Instead of running every point through a slow solver, we run a carefully chosen sample and train physics-informed neural surrogates that predict performance in milliseconds.
Step 3 — Search intelligently
With sub-second surrogates, our quantum-inspired optimization sweep evaluates thousands of candidate configurations in minutes, mapping out the true Pareto frontier.
Step 4 — Verify and decide
The best candidate designs from the surrogate search are automatically validated with high-fidelity solver runs, ensuring zero hallucination and ironclad auditability.
Deterministic Accuracy with 480x Speedup
Surrogate models are only useful if engineering teams can trust their output. Our physics-informed Fourier operators maintain an R² correlation > 0.998 against raw OpenFOAM and Ansys Fluent benchmarks.
Every design recommendation includes an uncertainty boundary and an automated verification solver pass before presentation.
Test your own simulation workflow
Bring us your hardest solver queue. We will set up a benchmark study on our GPU cluster.
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