Linear solvers · Packaging
Conjugate Gradient (CG) for Packaging
Apply the conjugate gradient (cg) to real packaging problems — in the browser, with AI assistance.
CG minimizes the residual over expanding Krylov subspaces, converging far faster than stationary methods for SPD systems common in FEM.
In packaging, teams face challenges like drop protection, material cost, sustainability. The conjugate gradient (cg) 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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