arXiv Machine Learning By Satoshi Noguchi, Yoshinobu Kawahara

Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics

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arXiv:2605. 00394v3 Announce Type: replace Abstract: We present Mesh Field Theory (MeshFT) and its neural realization, MeshFT-Net: a structure-preserving framework for mesh-based continuum physics that cleanly separates the physics' topological structure from its metric structure.

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arXiv Machine Learning
Jul 10

PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations

arXiv:2607. 08025v1 Announce Type: new Abstract: While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and the single node bottleneck, where the maximum processable mesh resolution is strictly limited by the VRAM of a single compute unit.

By Weiheng Zhong, Jing Bi, Victor Oancea, Hadi Meidani