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arXiv Machine Learning By Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, Mohammad Vahab, Cosmin Anitescu, Timon Rabczuk, Elena Atroshchenko

A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture

Read the original on arXiv Machine Learning →

The paper introduces a mesh‑free multiresolution deep energy method for phase‑field modeling of brittle fracture. A single neural network represents displacement and phase fields, trained by minimizing incremental energy with multiresolution B‑spline feature encoding and stratified Monte Carlo integration. Across six benchmark problems, the method reproduces load‑displacement curves and crack patterns with high accuracy, outperforming a deep Ritz baseline on a random multi‑crack dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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