MACHINE LEARNING drives design space exploration

Combining simulation with probabilistic ML enables engineers to chart the full design landscape, quantify uncertainty and uncover viable options that intuition and brute force alone would miss.

Components and systems are routinely designed and validated virtually through tools like CFD and FEA before any physical prototype is built. The benefits are obvious: faster iteration, reduced cost and better products.

But simulation is not cheap. Each run can take hours, consume costly GPU/CPU resources and require highly skilled engineers who are already in short supply. Licenses and compute costs can easily reach tens of thousands of dollars per seat, and most teams can complete only a few runs per day.