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.

使用多保真度神经网络解决反问题，通过纹理映射实现将机械性能的光学图像转化为真实电容力曲线。