DeepAI
Log In Sign Up

Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics

11/28/2020
by   Jiaxin Zhang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/13/2023

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental co...
07/08/2021

Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue

Constitutive models that describe the mechanical behavior of soft tissue...
07/27/2021

Physics-Enforced Modeling for Insertion Loss of Transmission Lines by Deep Neural Networks

In this paper, we investigate data-driven parameterized modeling of inse...
12/22/2017

Taking Visual Motion Prediction To New Heightfields

While the basic laws of Newtonian mechanics are well understood, explain...
01/20/2023

Benchmarks for physics-informed data-driven hyperelasticity

Data-driven methods have changed the way we understand and model materia...