  For multilayer materials in thin substrate systems, interfacial failure is
one of the most challenges. The traction-separation relations (TSR)
quantitatively describe the mechanical behavior of a material interface
undergoing openings, which is critical to understand and predict interfacial
failures under complex loadings. However, existing theoretical models have
limitations on enough complexity and flexibility to well learn the real-world
TSR from experimental observations. A neural network can fit well along with
the loading paths but often fails to obey the laws of physics, due to a lack of
experimental data and understanding of the hidden physical mechanism. In this
paper, we propose a thermodynamic consistent neural network (TCNN) approach to
build a data-driven model of the TSR with sparse experimental data. The TCNN
leverages recent advances in physics-informed neural networks (PINN) that
encode prior physical information into the loss function and efficiently train
the neural networks using automatic differentiation. We investigate three
thermodynamic consistent principles, i.e., positive energy dissipation,
steepest energy dissipation gradient, and energy conservative loading path. All
of them are mathematically formulated and embedded into a neural network model
with a novel defined loss function. A real-world experiment demonstrates the
superior performance of TCNN, and we find that TCNN provides an accurate
prediction of the whole TSR surface and significantly reduces the violated
prediction against the laws of physics.
