Vortic CFD builds custom machine learning for automotive and motorsport engineering: models trained on simulation data to predict how a design will perform, work out where to place a sensor, control a system in real time, or search a design space automatically.

That machine learning is built on top of real CFD: vehicle external aerodynamics, cooling and thermal airflow, and the design validation work that sits between a CAD model and a manufactured part. High-fidelity simulation is what generates the data every model is trained on.

Every engagement starts from a specific question a team is trying to answer. A model, a control policy, or a CFD study earns its place by changing a design decision.

Vortic CFD is led by Deen Akrivos, who founded the practice to bring machine learning and high-fidelity CFD together for teams building and refining real vehicles: motorsport programs, performance manufacturers, and the fabrication and tuning shops that support them.