An implicit Hessian-vector product algorithm for FEM-based differentiable physics, built from JAX JVP/VJP primitives and validated with finite differences and Taylor tests, accelerates Newton-CG on nonlinear inverse problems.
Jax md: a framework for differentiable physics
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Implicit differentiation with second-order derivatives and benchmarks in finite-element-based differentiable physics
An implicit Hessian-vector product algorithm for FEM-based differentiable physics, built from JAX JVP/VJP primitives and validated with finite differences and Taylor tests, accelerates Newton-CG on nonlinear inverse problems.