Gaussian Process Regression for Energy

GPs model outputs as draws from a distribution over functions, giving both predictions and calibrated uncertainty for expensive simulations.

In energy, teams face challenges like efficiency losses, thermal management, grid stability. The gaussian process regression directly supports use cases such as wind-turbine cfd, battery modeling, heat-exchanger design, and PolySim's AI Copilot can recommend settings and catch common setup errors before you run.