Surrogate · Packaging
Radial Basis Function (RBF) Interpolation for Packaging
Apply the radial basis function (rbf) interpolation to real packaging problems — in the browser, with AI assistance.
RBF interpolation fits smooth functions through scattered samples, a fast and accurate basis for surrogate models and meshless methods.
In packaging, teams face challenges like drop protection, material cost, sustainability. The radial basis function (rbf) interpolation 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.
Typical Packaging use cases
- Drop-test FEA
- Cushioning analysis
- Material optimization
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