Human fingerprints serve as one unique and powerful characteristic for each person, from which policemen can recognize the identity. Similar to humans, many natural bodies and intrinsic mechanical qualities can also be uniquely identified from surface characteristics. To measure the elasto-plastic properties of one material, one formally sharp indenter is pushed into the measured body under constant force and retracted, leaving a unique residual imprint of the minute size from several micrometers to nanometers. However, one great challenge is how to map the optical image of this residual imprint into the real wanted mechanical properties, i.e., the tensile force curve. In this paper, we propose a novel method to use multi-fidelity neural networks (MFNN) to solve this inverse problem. We first actively train the NN model via pure simulation data, and then bridge the sim-to-real gap via transfer learning. The most innovative part is that we use NN to dig out the unknown physics and also implant the known physics into the transfer learning framework, thus highly improving the model stability and decreasing the data requirement. This work serves as one great example of applying machine learning into the real experimental research, especially under the constraints of data limitation and fidelity variance.

翻译：人类指纹作为每个人独特且强大的特征，警方可据此识别身份。与人类相似，许多自然物体及内在力学特性也能通过表面特征进行唯一识别。为测量材料的弹塑性性能，通常将标准尖锐压头在恒定力作用下压入被测物体后撤回，留下微米至纳米量级的微小残余压痕。然而，如何将该残余压痕的光学图像映射至实际所需的力学性能（即拉伸力曲线）仍是一大挑战。本文提出一种利用多保真度神经网络（MFNN）解决该逆问题的新方法。我们首先通过纯仿真数据主动训练神经网络模型，再通过迁移学习弥合仿真与现实的差距。最具创新性的是，我们利用神经网络挖掘未知物理规律，同时将已知物理规律植入迁移学习框架，从而显著提升模型稳定性并降低数据需求。本研究为机器学习应用于真实实验研究提供了优秀范例，尤其适用于数据有限且保真度存在差异的约束场景。