We recently proposed a method called Material Fingerprinting for the rapid discovery of mechanical material models that avoids solving continuous optimization problems. Material Fingerprinting assumes that each material exhibits a unique response when subjected to a standardized experimental setup, which is interpreted as the material's mechanical fingerprint. If a database of fingerprints is generated in an offline phase, a model for an unseen experimental measurement can be discovered in real time by comparing the experimentally measured fingerprint to the fingerprints in the database. In our original contributions, the database comprised a fixed number of material models, each with a fixed number of parameters. To increase the fitting flexibility of Material Fingerprinting, we propose an adaptive model database coupled with an iterative pattern recognition algorithm that refines the material model in each step. This strategy enables Material Fingerprinting to discover arbitrary linear combinations of material models from the database, rather than being restricted to selecting a single model from a predefined set. In comparison to previous works on Material Fingerprinting, this enables the discovery of more complex models, such as multi-term Ogden models or the anisotropic Holzapfel-Gasser-Ogden model. To design the adaptive database, we leverage sums of strain energy density feature functions that depend on isotropic and anisotropic invariants. All modeling features satisfy fundamental physical constraints, and polyconvexity can be optionally enforced via a simple user-controlled switch. We test the method on experimental data stemming from mechanical tests of isotropic rubber materials and anisotropic animal skin tissue.

翻译：我们近期提出了一种名为"材料指纹识别"的方法，用于快速发现力学材料模型，该方法避免了求解连续优化问题。材料指纹识别假设，当每种材料在标准化实验设置中受载时，会表现出独特的响应特征，该特征即被视为该材料的力学指纹。若在离线阶段生成指纹数据库，则可通过将实验测得的指纹与数据库中的指纹进行比对，实时发现未知实验测量对应的材料模型。在我们前期的研究中，数据库包含固定数量的材料模型，且每个模型具有固定数量的参数。为提升材料指纹识别的拟合灵活性，我们提出了一种自适应模型数据库，并耦合迭代模式识别算法，该算法可在每一步中精化材料模型。这一策略使材料指纹识别能够发现数据库中材料模型的任意线性组合，而非局限于从预定义集合中选择单一模型。与先前的材料指纹识别研究相比，该方法能够发现更复杂的模型，如多阶奥格登模型或各向异性霍尔扎普费尔-加瑟-奥格登模型。为了设计自适应数据库，我们利用了依赖于各向同性与各向异性不变量项的应变能密度特征函数之和。所有建模特征均满足基本物理约束，且可通过简单的用户控制开关选择性地施加多凸性条件。我们采用各向同性橡胶材料与各向异性动物皮肤组织的力学实验数据对方法进行了验证。