A machine-learning framework for peridynamic material models with physical constraints

TL;DR

This paper introduces a machine-learning framework that uses data-driven regression with physical constraints to develop accurate bond-based peridynamic models for heterogeneous elastic materials, reducing complex derivations.

Contribution

It presents a novel regression approach incorporating physical energy constraints to efficiently determine influence functions in peridynamics for heterogeneous materials.

Findings

Energy-constrained models improve accuracy

Method effective for 1D and 2D elastodynamics

Reduces need for complex analytical derivations

Abstract

As a nonlocal extension of continuum mechanics, peridynamics has been widely and effectively applied in different fields where discontinuities in the field variables arise from an initially continuous body. An important component of the constitutive model in peridynamics is the influence function which weights the contribution of all the interactions over a nonlocal region surrounding a point of interest. Recent work has shown that in solid mechanics the influence function has a strong relationship with the heterogeneity of a material's micro-structure. However, determining an accurate influence function analytically from a given micro-structure typically requires lengthy derivations and complex mathematical models. To avoid these complexities, the goal of this paper is to develop a data-driven regression algorithm to find the optimal bond-based peridynamic model to describe the macro-scale deformation of linear elastic medium with periodic heterogeneity. We generate macro-scale deformation training data by averaging over periodic micro-structure unit cells and add a physical energy constraint representing the homogenized elastic modulus of the micro-structure to the regression algorithm. We demonstrate this scheme for examples of one- and two-dimensional linear elastodynamics and show that the energy constraint improves the accuracy of the resulting peridynamic model.