Uncover Hidden Physical Information of Soft Matter by Observing Large Deformation

Abstract

Accurate and non-destructive detection of material abnormalities inside soft matter remains an elusive challenge due to its variable and heterogeneous nature, especially regarding non-visual information. Here, a method is introduced that uncovers the physical information of internal material abnormalities from large deformations observed on the surface of the soft object. It finds the most probable values of imperceptible physical parameters by matching the nonlinear surface deformation between observation and finite element simulation through parallel Bayesian optimization, balancing the trade-off between simulation accuracy and computational efficiency. Numerical and experimental tests, including simulated cases of aortic valve calcification, are conducted to showcase the effectiveness of our method, where we successfully recover hidden physical parameters including material stiffness, abnormality shape, and location. The method holds substantial promise for advancing the fields of material perception of robots, soft robotics, biology, and medical diagnostics, offering a powerful tool for the precise, efficient, and non-invasive analysis of soft matter.