Non-invasive inference of thrombus material properties with physics-informed neural networks

We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs to extract the permeability and viscoelastic modulus from thrombus deformation data, which can be described by the fourth-order Cahn-Hilliard and Navier-Stokes Equations. In PINNs, the partial differential equations are encoded into a loss function, where partial derivatives can be obtained through automatic differentiation (AD). To tackle the challenge of calculating the fourth-order derivative in the Cahn-Hilliard equation with AD, we introduce an auxiliary network along with the main neural network to approximate the second-derivative of the energy potential term. Our model can simultaneously predict unknown material parameters and velocity, pressure, and deformation gradient fields by merely training with partial information among all data, i.e., phase field and pressure measurements, while remaining highly flexible in sampling within the spatio-temporal domain for data acquisition. We validate our model by numerical solutions from the spectral/hp element method (SEM) and demonstrate its robustness by training it with noisy measurements. Our results show that PINNs can infer the material properties from noisy synthetic data and thus they have great potential for inferring these properties from experimental multi-modality and multi-fidelity data. Published by Elsevier B.V.

Automated summary

The study utilizes physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data, successfully applied to extract permeability and viscoelastic modulus from thrombus deformation data. The results demonstrate that PINNs can infer material properties from noisy synthetic data and have great potential for inferring these properties from experimental multi-modality and multi-fidelity data.