This is a case study in making gradient-based optimization (a la gradient descent) work with tools that weren’t designed for it. The goal is optimizing rocket grid fin stiffness (8 bars, 16 angular parameters), with a pipeline that includes Ansys SpaceClaim (CAD), PyMAPDL (FEM solver), and JAX. Each step needs derivatives, which means stitching together analytical adjoints from Ansys, finite differences for mesh operations, and JAX autodiff for everything else. What makes this practical is that each component runs in its own isolated environment (Tesseract), so the Ansys tools can live on Windows with their license requirements while the optimization logic runs on Linux while still being composable for end-to-end autodiff.
The payoff is watching emergent behavior: starting from random bar positions, the optimizer discovers that orthogonal grid patterns work well, diagonal lateral bars create efficient load paths, and clustering material near the attachment points maximizes stiffness. Final result is 75% stiffer than random and 24% better than a regular grid, even after adding back symmetry constraints for manufacturing.
dionhaefner•2mo ago
The payoff is watching emergent behavior: starting from random bar positions, the optimizer discovers that orthogonal grid patterns work well, diagonal lateral bars create efficient load paths, and clustering material near the attachment points maximizes stiffness. Final result is 75% stiffer than random and 24% better than a regular grid, even after adding back symmetry constraints for manufacturing.