Material Fingerprinting is a lookup table-based strategy to discover material models from experimental measurements, which completely avoids the need to solve an optimization problem. In an offline phase, a comprehensive database of simulated material responses, so-called material fingerprints, is generated for a predefined experimental setup. This database can then be used repeatedly in the online phase to discover material models corresponding to experimentally measured observations. To this end, the experimentally measured fingerprint is compared with all fingerprints in the database to identify the closest match. The primary advantage of this strategy is that it does not require solving a continuous optimization problem. This avoids the associated computational costs as well as issues of ill-posedness caused by local minima in non-convex optimization landscapes. Material Fingerprinting has been successfully demonstrated for supervised datasets consisting of stress-strain pairs, as well as for unsupervised datasets involving full-field displacements and net reaction forces. However, to date, there is no experimental validation for the latter approach which is the objective of this work.

翻译：材料指纹识别是一种基于查找表的策略，用于从实验测量中发现材料模型，该方法完全避免了求解优化问题的需求。在离线阶段，针对预定义的实验设置，生成一个包含模拟材料响应（即所谓的材料指纹）的综合性数据库。随后，在在线阶段，该数据库可重复用于发现与实验测量观测值对应的材料模型。为此，将实验测量的指纹与数据库中的所有指纹进行比较，以识别最接近的匹配项。该策略的主要优势在于无需求解连续优化问题，从而避免了相关的计算成本以及由非凸优化景观中的局部极小值引起的不适定问题。材料指纹识别已成功应用于由应力-应变对组成的监督数据集，以及涉及全场位移和净反作用力的无监督数据集。然而，迄今为止，后一种方法尚未得到实验验证，这正是本工作的目标所在。