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arXiv Machine Learning By Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling

Read the original on arXiv Machine Learning →

arXiv:2606. 19378v1 Announce Type: new Abstract: Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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