ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Uncover Hidden Physical Information of Soft Matter by Observing Large Deformation.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- FEM-Based Estimation-Correction with Minimal Indentation Set for Internal Cavity Classification and Geometry Estimation in Deformable Objects.Sensors (Basel, Switzerland) · 2026Article
- Uncover Hidden Physical Information of Soft Matter by Observing Large Deformation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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.
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Registered trials
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