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

An extended deep energy method for thermo-mechanical crack propagation

Read the original on arXiv Machine Learning →

The paper introduces an extended deep energy method for modeling thermo‑mechanical crack propagation, keeping the crack as a sharp polyline. Two neural networks predict temperature and displacement, receiving the crack via a scalar embedding that allows discontinuities without regularization, and the displacement is enriched near the tip using a trainable Williams expansion. The method minimizes incremental conduction and thermoelastic energy, employs Monte Carlo integration, and extracts stress intensity factors through an interaction integral, validating results against published benchmarks for thermal edge cracks, graded shear tests, and notched cruciform specimens.

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