A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

by   Huaiqian You, et al.

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledges on the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on the phenomenological Fung-type model. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.


page 9

page 10


Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling

Constitutive modeling based on continuum mechanics theory has been a cla...

Magnetic Field Simulation with Data-Driven Material Modeling

This paper developes a data-driven magnetostatic finite-element (FE) sol...

Intelligent multiscale simulation based on process-guided composite database

In the paper, we present an integrated data-driven modeling framework ba...

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

An innovative physics-guided learning algorithm for predicting the mecha...

Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

Physics-constrained data-driven computing is an emerging computational p...

Global forensic geolocation with deep neural networks

An important problem in forensic analyses is identifying the provenance ...

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning

Finite element methods (FEM) are popular approaches for simulation of so...

Please sign up or login with your details

Forgot password? Click here to reset