The recent innovations of computational material models by machine learning (ML) methods face formidable challenges. Incorporating internal heterogeneity and diverse boundary conditions (BC’s) into existing ML methods remains difficult, and the weak interpretability of ML remains unresolved. To tackle these challenges, this paper generalizes a recently developed self-evolving computational material models framework built upon Bayesian update and evolutionary algorithm. This paper proposes a new material-specific information index (II), which is capable of autonomously quantifying the internal heterogeneity and diverse BC’s. Also, this paper introduces highly flexible cubic regression spline (CRS)-based link functions which can offer mathematical expressions of salient material coefficients of the existing computational material models in terms of convolved II. Thereby, this paper suggests a novel means by which ML can directly leverage internal heterogeneity and diverse BC’s to autonomously evolve computational material models while keeping interpretability. Validations using a wide spectrum of large-scale reinforced composite structures confirm the favorable performance of the generalization. Example expansions of nonlinear shear of quasi-brittle materials and progressive compressive buckling of reinforcing steel underpin efficiency and accuracy of the generalization. This paper adds a meaningful avenue for accelerating the fusion of computational material models and ML.

最近通过机器学习 (ML) 方法对计算材料模型进行的创新面临着巨大的挑战。将内部异质性和不同的边界条件 (BC) 纳入现有的 ML 方法仍然很困难，并且 ML 的弱可解释性仍未解决。为了应对这些挑战，本文概括了最近开发的基于贝叶斯更新和进化算法的自进化计算材料模型框架。本文提出了一种新的材料特定信息指数（II），它能够自主量化内部异质性和多样化的 BC。还，本文介绍了高度灵活的基于三次回归样条 (CRS) 的链接函数，它可以根据卷积 II 提供现有计算材料模型的显着材料系数的数学表达式。因此，本文提出了一种新方法，ML 可以通过该方法直接利用内部异质性和不同的 BC 来自主演化计算材料模型，同时保持可解释性。使用广泛的大型增强复合结构的验证证实了泛化的良好性能。准脆性材料的非线性剪切和钢筋的渐进压缩屈曲的示例扩展支持效率和概括的准确性。本文为加速计算材料模型和机器学习的融合增加了一条有意义的途径。