Data-Driven Solvers for Strongly Nonlinear Material Response

TL;DR

This paper introduces a local weighting approach in a data-driven magnetostatic finite-element solver, significantly enhancing accuracy and efficiency when dealing with strongly nonlinear materials and unbalanced data sets.

Contribution

The work extends existing data-driven solvers with heterogeneous weighting factors, improving handling of unbalanced data and nonlinear material responses.

Findings

Major improvements in solution accuracy with local weighting

Order of magnitude faster convergence with noiseless data

Doubled convergence rate with noisy data

Abstract

This work presents a data-driven magnetostatic finite-element solver that is specifically well-suited to cope with strongly nonlinear material responses. The data-driven computing framework is essentially a multiobjective optimization procedure matching the material operation points as closely as possible to given material data while obeying Maxwell's equations. Here, the framework is extended with heterogeneous (local) weighting factors - one per finite element - equilibrating the goal function locally according to the material behavior. This modification allows the data-driven solver to cope with unbalanced measurement data sets, i.e. data sets suffering from unbalanced space filling. This occurs particularly in the case of strongly nonlinear materials, which constitute problematic cases that hinder the efficiency and accuracy of standard data-driven solvers with a homogeneous (global) weighting factor. The local weighting factors are embedded in the distance-minimizing data-driven algorithm used for noiseless data, likewise for the maximum entropy data-driven algorithm used for noisy data. Numerical experiments based on a quadrupole magnet model with a soft magnetic material show that the proposed modification results in major improvements in terms of solution accuracy and solver efficiency. For the case of noiseless data, local weighting factors improve the convergence of the data-driven solver by orders of magnitude. When noisy data are considered, the convergence rate of the data-driven solver is doubled.