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Parametric shape optimization with differentiable FEM simulation

In this example, you will learn how to: Build a Tesseract that wraps a differentiable finite-element solver from jax-fem. Build a Tesseract that uses finite differences under the hood to enable differentiability of a non-autodifferentiable geometry operation (computing a signed distance field from a 3D model). Compose both Tesseracts with Tesseract-JAX to create a pipeline that can be used for differentiable shape optimization. Perform gradient-based optimization using optax on the Tesseract-JAX...

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