Citation :

M. VanithaLakshmi, P.S.G. Aruna Sri, Kalaivani P, Vinoth Ramalingam, "Ensemble Kalman Filter–based Multi-Parameter Identification of Dislocation Mobility in Discrete Dislocation Dynamics," International Journal of Mechanical Engineering, vol. 13, no. 7, pp. 255-265, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I7P120

Abstract

To enhance the predictive ability of Discrete Dislocation Dynamics (DDD) simulations applied to study plastic deformation and microstructural evolution in crystalline materials, it is necessary to identify the dislocation mobility parameters accurately. This paper introduces an inverse modeling algorithm, which is based on the EnKF, for the joint estimation of screw, edge, and climb mobility parameters based on the observations of the trajectory of the dislocation nodes. The proposed solution incorporates both physics-based DDD forward model and sequential data assimilation by means of an augmented-state formulation that encompasses and defines both dislocation node coordinates and unknown mobility parameters. The forward simulations of DDD with known reference parameters and controlled measurement noise are used to generate synthetic observation data in order to allow the systematic validation of the framework. The convergence of parameters, the reconstruction accuracy, dependence on observation noise, dependence on ensemble size, and uncertainty quantification are some of the performance measures of the proposed method. Findings show that the EnKF system can effectively reconstruct the target mobility parameters with relative errors less than 1 percent and, at the same time, enhance the reconstruction of the changing dislocation structures. The uncertainty analysis also shows that there are no significant changes in the estimates of the parameters with close confidence intervals in the conditions of the synthetic benchmarks considered. The suggested framework offers an effective and uncertainty-sensitive methodology to identify mobility parameters and illustrates the promise of integrating physics-based methods to compute dislocation dynamics into ensemble data assimilation algorithms to make inferences about materials through computational methods.

Keywords

Discrete Dislocation Dynamics, Ensemble Kalman Filter, Mobility Parameter Estimation, Data Assimilation, Uncertainty quantification.

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