A machine learning strategy for computing interface curvature in Front-Tracking methods
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In this work we have described the application of a machine learning strategy to compute the interface curvature in the context of a Front-Tracking framework. Based on angular information of normal and tangential vectors between marker points, the interface curvature is predicted using a neural network. The Front-Tracking-Machine-Learning method is validated using a sine wave and then applied in combination with a Marker-And-Cell method for solving a complex free surface flow. Our results indicate that it is feasible to employ machine learning concepts as an alternative approach for computing curvatures in Front-Tracking schemes.
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Journal of Computational Physics, v. 450.
