Pith. sign in

REVIEW 3 cited by

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.03541 v2 pith:X44MSZRH submitted 2024-02-05 cs.LG cs.NAmath.NA

Andrey Bryutkin , Jiahao Huang , Zhongying Deng , Guang Yang , Carola-Bibiane Schönlieb , Angelica Aviles-Rivero This is my paper
classification cs.LGcs.NAmath.NA
keywords hamletdifferentialframeworkgraphpdesdataequationsinput
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks. The framework uses graph transformers with modular input encoders to directly incorporate differential equation information into the solution process. This modularity enhances parameter correspondence control, making HAMLET adaptable to PDEs of arbitrary geometries and varied input formats. Notably, HAMLET scales effectively with increasing data complexity and noise, showcasing its robustness. HAMLET is not just tailored to a single type of physical simulation, but can be applied across various domains. Moreover, it boosts model resilience and performance, especially in scenarios with limited data. We demonstrate, through extensive experiments, that our framework is capable of outperforming current techniques for PDEs.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Accurate and scalable deep Maxwell solvers using multilevel iterative methods

    physics.comp-ph 2025-09 conditional novelty 7.0 of 10

    A neural subdomain preconditioner plus multilevel domain decomposition solves 2D Maxwell problems up to 200 wavelengths and drives inverse design of large nanophotonic devices.

  2. GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations

    cs.LG 2025-06 conditional novelty 5.0 of 10

    GITO, a graph-informed transformer operator, reports lower relative L2 errors than existing transformer-based neural operators on Navier-Stokes, heat conduction, and airfoil benchmark datasets.

  3. GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer

    cs.LG 2025-12 conditional novelty 4.0 of 10

    GeoTransolver, a geometry-aware attention transformer, improves surrogate CFD accuracy over existing baselines on three automotive/aerospace datasets, but the paper has major reporting gaps.

Pith tools