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

Time: 2021-09-08 23:01:00

Topic：Non-invasive inference of thrombus material properties with physics-informed neural network

Speaker：Xiaoning Zheng      Jinan University 

Time：2021-09-08 10:00--11:30

Location：Room 401

Introduction：

    We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring 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). In addition to tackling 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.