arXiv AI August 17, 2026 By Wenqian Chen, Zhi-Feng Wei, Yucheng Fu, Michael Penwarden, Pratanu Roy, Panos Stinis

ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning

arXiv:2602. 11626v3 Announce Type: replace-cross Abstract: Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and evolving geometries requiring accurate, geometry-aware predictions at arbitrary spatial locations.

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

arXiv Machine Learning

Jul 2

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems

arXiv:2607. 01128v1 Announce Type: new Abstract: Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation.

By Meenakshi Krishnan, Pranav Pulijala, Ke Chen, Haizhao Yang, Ramani Duraiswami

llms benchmarks

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

benchmarks safety

arXiv Machine Learning

Jul 8

PGOT: A Physics-Geometry Operator Transformer for Complex PDEs

arXiv:2512. 23192v4 Announce Type: replace Abstract: While Transformers have demonstrated remarkable potential in modeling Partial Differential Equations (PDEs), modeling large-scale unstructured meshes with complex geometries remains a significant challenge.

By Zhuo Zhang, Xi Yang, Ying Miao, Xiaobin Hu, Yifu Gao, Yong Yang, Canqun Yang, Boocheong Khoo

llms benchmarks

Hugging Face Trending Papers

Jul 9

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

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. To address these challenges, we propose PGD-NO, a neural operator with Precomputed Geometry Decomposition, that relocates the computational overhead of geometric encoding to a deterministic pre-computation phase.

benchmarks safety

arXiv Machine Learning

Jun 15

Zero-shot generalization of transformer neural operators to larger domains

arXiv:2606. 14597v1 Announce Type: new Abstract: Transformer-based neural operators have shown remarkable performance for approximating solution operators of partial differential equations on complex geometries.

By Armand de Villeroch\'e, Sibo Cheng, Vincent Le Guen, Marc Bocquet, Rem-Sophia Mouradi, Patrick Armand, Alban Farchi, Patrick Massin

llms rag benchmarks safety

arXiv AI

Jun 17

Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers

arXiv:2602. 07429v2 Announce Type: replace-cross Abstract: Boundary representation (B-rep) is the industry standard for computer-aided design (CAD).

By Yuanxu Sun, Yuezhou Ma, Haixu Wu, Guanyang Zeng, Muye Chen, Jianmin Wang, Mingsheng Long

llms benchmarks safety

Related stories

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems

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

PGOT: A Physics-Geometry Operator Transformer for Complex PDEs

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

Zero-shot generalization of transformer neural operators to larger domains

Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers