论文标题：Parametric Reduced-Order Constitutive Model for High-Strain-Rate and Temperature-Dependent Behavior

论文作者：Henglei Quan, Qiang Chen*, Zhengyuan Hu, Wenhe Wang, Liucheng Zhou, Zhibo Yang

摘要：We present a multi-parameter reduced-order constitutive model (ROCM) for real-time characterization of metallic material behavior under high-strain-rate loading and elevated temperature. To this end, the proper orthogonal decomposition (POD) technique is utilized to decompose stress-strain responses generated from the Johnson-Cook (J-C) constitutive model and extract their principal components, referred to as POD modes. Multiple neural networks are then trained to learn the nonlinear mapping between the parametric vectors and the corresponding POD projection coefficients, enabling efficient reconstruction of stress-strain curves directly from the input parameters. To enhance the predictive performance of the proposed framework, the loss function is formulated to include the residuals of the reconstructed stress-strain responses and their corresponding energy contributions, in addition to the deviations in the POD projection coefficients. The proposed ROCM framework is encapsulated within a Python-based user interface (UI), enabling intuitive calibration of material parameters against experimental data and real-time prediction of stress-strain curves across various material parameters and loading conditions. The effectiveness of the proposed ROCM is showcased through extensive comparisons with direct numerical simulations and with experimental results. Moreover, the ROCM achieves a speedup of approximately five orders of magnitude in the online stage compared to the direct numerical analysis, making it highly suitable for large-scale parametric studies.

这篇论文是全恒磊同学研一第二篇发表的论文，祝贺