Effective Data Sampling Strategies and Boundary Condition Constraints of Physics-Informed Neural Networks for Identifying Material Properties in Solid Mechanics

Understand

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.

• However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue.
• In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs).
• To improve the accuracy and efficiency of PINNs, we developed efficient strategies to nonuniformly sample observational data.