We present the formulation and applications of JAX-CPFEM,an open-source,GPU-accelerated,and differentiable 3-D crystal plasticity finite element method(CPFEM)software package.Leveraging the modern computing architectu...We present the formulation and applications of JAX-CPFEM,an open-source,GPU-accelerated,and differentiable 3-D crystal plasticity finite element method(CPFEM)software package.Leveraging the modern computing architecture JAX,JAX-CPFEM features high performance through array programming and GPU acceleration,achieving a 39×speedup in a polycrystal case with~52,000 degrees of freedom compared to MOOSE with MPI(8 cores).Furthermore,JAX-CPFEM utilizes the automatic differentiation technique,enabling users to handle complex,non-linear constitutive materials laws without manually deriving the case-specific Jacobian matrix.Beyond solving forward problems,JAX-CPFEM demonstrates its potential in an inverse design pipeline,where initial crystallographic orientations of polycrystal copper are optimized to achieve targeted mechanical properties under deformations.The end-to-end differentiability of JAX-CPFEM allows automatic sensitivity calculations and high-dimensional inverse design using gradient-based optimization.The concept of differentiable JAX-CPFEM provides an affordable,flexible,and multi-purpose tool,advancing efficient and accessible computational tools for inverse design in smart manufacturing.展开更多
基金support from the Department of Defense Vannevar Bush Faculty Fellowship,USA N00014-19-1-2642from the NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution(ERC‐HAMMER)under Award Number EEC-2133630.
文摘We present the formulation and applications of JAX-CPFEM,an open-source,GPU-accelerated,and differentiable 3-D crystal plasticity finite element method(CPFEM)software package.Leveraging the modern computing architecture JAX,JAX-CPFEM features high performance through array programming and GPU acceleration,achieving a 39×speedup in a polycrystal case with~52,000 degrees of freedom compared to MOOSE with MPI(8 cores).Furthermore,JAX-CPFEM utilizes the automatic differentiation technique,enabling users to handle complex,non-linear constitutive materials laws without manually deriving the case-specific Jacobian matrix.Beyond solving forward problems,JAX-CPFEM demonstrates its potential in an inverse design pipeline,where initial crystallographic orientations of polycrystal copper are optimized to achieve targeted mechanical properties under deformations.The end-to-end differentiability of JAX-CPFEM allows automatic sensitivity calculations and high-dimensional inverse design using gradient-based optimization.The concept of differentiable JAX-CPFEM provides an affordable,flexible,and multi-purpose tool,advancing efficient and accessible computational tools for inverse design in smart manufacturing.
