| description abstract | Abstract. Accurate and efficient determination of crystal plasticity (CP) material parameters is essential for predictive simulations that link microstructures, manufacturing processes, and material properties. This study presents a graphical processing unit (GPU)-accelerated pipeline for calibrating CP material parameters, integrating automatic differentiation (AD)-based sensitivities with gradient-based optimization, built upon our open-source jax-cpfem package. This method eliminates reliance on finite differences in gradient-based approaches while improving efficiency over gradient-free optimization. The effectiveness of the pipeline is demonstrated through five case studies covering various crystal structures and boundary conditions. First, the AD-based sensitivity analysis achieves over 10 × speedup compared to finite difference while maintaining accuracy for complex, nonlinear constitutive laws. Second, a comprehensive analysis of initial starting points on gradient-based optimization demonstrates that using appropriate bounds mitigates potential issues. Across both single-crystal and polycrystalline cases calibrating six material parameters, our pipeline requires approximately 7 × fewer iterations and achieves 3 × higher efficiency over popular gradient-free methods like Bayesian optimization, regardless of geometry complexity. Furthermore, the successful calibration of 12 parameters in a dual-phase steel model highlights the capability of the pipeline to handle high-dimensional optimization problems, which is challenging for gradient-free optimization. Finally, the robustness of our pipeline is validated using noisy synthetic data and experimental tensile data for wrought IN625 over a finite strain range. These results illustrate the applicability of our pipeline to real-world scenarios and its potential for high-dimensional optimization and promising applications in integrated computational materials engineering workflows. | |
