Gaussian Process Regression for Packaging

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

In packaging, teams face challenges like drop protection, material cost, sustainability. The gaussian process regression directly supports use cases such as drop-test fea, cushioning analysis, material optimization, and PolySim's AI Copilot can recommend settings and catch common setup errors before you run.