Phase-field models of fracture in fibre-reinforced shells are usually formulated against a single closed-form stored energy, so that the stress, consistent tangent and plane-stress condensation are derived and verified for that expression alone. Here the constitutive law enters instead as a replaceable operator. The Gr...
Altaf Ahmad Lone, R. D. Durai Prabhakaran, T. Rabczuk et al.· 0 citations
This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly into the neural operator architecture, so that the operator structure matches the kernel-expansion form used in boundary-type kernel-expansion methods. Unlike traditio...
Yuan Guo, Han-Shu Chen, Qiang Xi et al.· 0 citations
This work systematically revisit the Deep Energy Method, placing it in the broader context of physics-informed learning and variational modeling, and identifies the class of problems for which energy minimization provides intrinsic advantages in terms of stability, robustness and interpretability.
Because training needs no labels, FEVessel works exactly where FEM cannot supply any, removing manual mesh repair from the analysis pipeline, and is the first systematic study of mesh-independent solution on industrially relevant 3D pressure vessels with degenerate meshes.
Yipin Sun, Yi-Zheng Wang, Yuzhou Lin et al.· arXiv.org· 0 citations
Plasolver, a physics-informed neural operator framework that combines the efficiency of operator learning with the accuracy and robustness of classical numerical solvers, provides an efficient, accurate, and discretization-invariant computational framework for nonlinear, path-dependent elastoplastic problems.
Yi-Zheng Wang, M. Eshaghi, Hua-Dong Zhang et al.· 0 citations
Numerical benchmarks show substantial accuracy of WINO together with total training times of about 15%-70% of those of supervised $\varphi$-FEM-FNO across all cases, without requiring reference-solution generation.
A mesh-free discretization in which a single neural network represents the displacement and phase fields and is trained by minimizing the incremental energy directly is proposed.
Han Zhang, M. Alamdari, B. Shahbodagh et al.· 1 citation
High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control. We develop neural-operator surrogates for temporal prediction of the hydrodynamic fields gene...
M. Eshaghi, Yi-Zheng Wang, N. Valizadeh et al.· 0 citations
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