  We employ physics-informed neural networks (PINNs) to infer properties of
biological materials using synthetic data. In particular, we successfully apply
PINNs on inferring the thrombus permeability and visco-elastic 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 the 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 predict simultaneously unknown parameters and velocity, pressure, and
deformation gradient fields by merely training with partial information among
all data, i.e., phase-field and pressure measurements, and is also highly
flexible in sampling within the spatio-temporal domain for data acquisition. We
validate our model by numerical solutions from the spectral/\textit{hp} element
method (SEM) and demonstrate its robustness by training it with noisy
measurements. Our results show that PINNs can accurately infer the material
properties with noisy synthetic data, and thus they have great potential for
inferring these properties from experimental multi-modality and multi-fidelity
data.
