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

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.

arXiv comment24 pages, 16 figures

References in corpus (3)

• Data-driven learning of robust nonlocal physics from high-fidelity synthetic data
• A cookbook for finite element methods for nonlocal problems, including quadrature rules and approximate Euclidean balls
• Deriving peridynamic influence functions for one-dimensional elastic materials with periodic microstructure

Cited by in corpus (5)

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• A FETI approach to domain decomposition for meshfree discretizations of nonlocal problems
• Efficient quadrature rules for finite element discretizations of nonlocal equations
• ML-based identification of the interface regions for coupling local and nonlocal models
• Efficient optimization-based quadrature for variational discretization of nonlocal problems