  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, \ie, 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 build up the NN model via pure
simulation data, and then bridge the sim-to-real gap via transfer learning.
Considering the difficulty of collecting real experimental data, 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. The final constructed model only needs
three-shot calibration of real materials. We tested the final model across 20
real materials and achieved satisfying accuracy. This work serves as one great
example of applying machine learning into scientific research, especially under
the constraints of data limitation and fidelity variance.
