Pith Number

pith:I5PBLHCI

pith:2024:I5PBLHCISM53LBVGUS5CSQOQBD

not attested not anchored not stored refs resolved

Transolver: A Fast Transformer Solver for PDEs on General Geometries

Haixu Wu, Haowen Wang, Huakun Luo, Jianmin Wang, Mingsheng Long

By grouping mesh points with similar physical states into learnable slices, Transolver lets Transformers solve PDEs on arbitrary geometries in linear time.

arxiv:2402.02366 v2 · 2024-02-04 · cs.LG · cs.NA · math.NA

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Transolver achieves consistent state-of-the-art with 22% relative gain across six standard benchmarks and also excels in large-scale industrial simulations, including car and airfoil designs.

C2weakest assumption

That mesh points under similar physical states can be reliably and adaptively grouped into a series of learnable slices whose encoded tokens capture the necessary physical correlations without missing critical local interactions.

C3one line summary

Transolver learns intrinsic physical states from discretized meshes by adaptively splitting domains into flexible learnable slices and computing attention over physics-aware tokens, achieving state-of-the-art PDE solving on general geometries.

References

33 extracted · 33 resolved · 4 Pith anchors

[1] GPT-4 Technical Report · arXiv:2303.08774

[2] ShapeNet: An Information-Rich 3D Model Repository · arXiv:1512.03012

[3] Training Deep Nets with Sublinear Memory Cost · arXiv:1604.06174

[4] Neural Operator: Graph Kernel Network for Partial Differential Equations 2003 · arXiv:2003.03485

[5] Geometry-informed neural operator for large-scale 3d pdes 2003

Formal links

2 machine-checked theorem links

Cited by

24 papers in Pith

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics

Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation

SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design

Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows

Receipt and verification

First computed	2026-05-17T23:38:50.065373Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

475e159c48933bb586a6a4ba2941d008d3e5607e0a538af8f65035d327d310a6

Aliases

arxiv: 2402.02366 · arxiv_version: 2402.02366v2 · doi: 10.48550/arxiv.2402.02366 · pith_short_12: I5PBLHCISM53 · pith_short_16: I5PBLHCISM53LBVG · pith_short_8: I5PBLHCI

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/I5PBLHCISM53LBVGUS5CSQOQBD \
  | 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: 475e159c48933bb586a6a4ba2941d008d3e5607e0a538af8f65035d327d310a6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7bc5f6994e368c87d7d5131fb94f441ea35873ed905b7481f22811602b775829",
    "cross_cats_sorted": [
      "cs.NA",
      "math.NA"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2024-02-04T06:37:38Z",
    "title_canon_sha256": "1d220a973c657e8af81c81d5fd504f3a1c2281406a4885737ee3ee7d591b8003"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2402.02366",
    "kind": "arxiv",
    "version": 2
  }
}