JetSCI is a hybrid JAX-PETSc framework that delivers scalable differentiable finite element simulations and outperforms pure JAX implementations on heterogeneous micromechanics problems.
Physics-driven machine learning models coupling pytorch and firedrake
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Automated reverse-mode adjoints are added to the Dedalus spectral PDE framework via symbolic graph differentiation, enabling gradient-based optimization for a wide range of time-dependent nonlinear problems without manual adjoint coding.
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JetSCI: A Hybrid JAX-PETSc Framework for Scalable Differentiable Simulation
JetSCI is a hybrid JAX-PETSc framework that delivers scalable differentiable finite element simulations and outperforms pure JAX implementations on heterogeneous micromechanics problems.
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Fast automated adjoints for spectral PDE solvers
Automated reverse-mode adjoints are added to the Dedalus spectral PDE framework via symbolic graph differentiation, enabling gradient-based optimization for a wide range of time-dependent nonlinear problems without manual adjoint coding.