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pith:DB574VAC

pith:2026:DB574VACKKTNCDLUEXZDT5DN26

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ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning

Michael Penwarden, Panos Stinis, Pratanu Roy, Wenqian Chen, Yucheng Fu

A transformer encodes arbitrary geometries from point clouds and serves as the trunk in DeepONet to learn operators that depend on both geometry and other inputs without explicit geometry parametrization in the branch.

arxiv:2602.11626 v2 · 2026-02-12 · cs.LG · cs.AI · physics.chem-ph · physics.comp-ph · physics.flu-dyn

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Claims

C1strongest claim

By integrating ArGEnT into DeepONet as the trunk network, the framework learns operator mappings that depend on both geometric and non-geometric inputs without the need to explicitly parametrize geometry as a branch network input, achieving significantly improved prediction accuracy and generalization on benchmarks spanning fluid dynamics, solid mechanics and electrochemical systems.

C2weakest assumption

That point-cloud representations processed by transformer attention can reliably capture the geometric features needed for accurate operator learning across arbitrary domains without additional explicit parametrization or signed-distance inputs.

C3one line summary

ArGEnT adds self-, cross-, and hybrid-attention transformers to DeepONet to learn geometry-dependent operators from point-cloud inputs, yielding higher accuracy than standard DeepONet on fluid, solid, and electrochemical benchmarks.

References

42 extracted · 42 resolved · 1 Pith anchors

[1] P. Benner, S. Gugercin, K. Willcox, A survey of projection-based model reduc- tion methods for parametric dynamical systems, SIAM review 57 (4) (2015) 63 483–531 2015

[2] Jameson, Aerodynamic shape optimization using the adjoint method, Lec- tures at the Von Karman Institute, Brussels 6 (2003) 2003

[3] Y. Sun, U. Sengupta, M. Juniper, Physics-informed deep learning for simultane- ous surrogate modeling and pde-constrained optimization of an airfoil geometry, Computer Methods in Applied Mechanics and 2023

[4] J.Sokolowski, J.-P.Zolésio, Introductiontoshapeoptimization, in: Introduction to Shape Optimization: Shape Sensitivity Analysis, Springer, 1992, pp. 5–12 1992

[5] D. Samadian, I. B. Muhit, N. Dawood, Application of data-driven surrogate models in structural engineering: a literature review, Archives of Computational Methods in Engineering 32 (2) (2025) 735–784 2025

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Receipt and verification

First computed	2026-05-17T23:38:59.964591Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

187bfe540252a6d10d7425f239f46dd7a91b98ab8a759fad89ab7229b8abcb7a

Aliases

arxiv: 2602.11626 · arxiv_version: 2602.11626v2 · doi: 10.48550/arxiv.2602.11626 · pith_short_12: DB574VACKKTN · pith_short_16: DB574VACKKTNCDLU · pith_short_8: DB574VAC

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/DB574VACKKTNCDLUEXZDT5DN26 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 187bfe540252a6d10d7425f239f46dd7a91b98ab8a759fad89ab7229b8abcb7a

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-12T06:22:59Z",
    "title_canon_sha256": "51be1cdbdcab978acb9ad3e55e167b9834e90fd0d450bde49b1cf7c7a60902ff"
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