Paper Citation Record · LEDGER

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining

As of 21 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2501.09597.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.09597 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:55:00.210080Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact2
verified fuzzy15
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67dc1f79-7998-4b9f-87ce-690b249dd0d0 · outbound

This paper cites an unresolved cited work.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:55:00.748567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a592ef7a-87da-4efd-83d7-531168fd1cd5 · outbound

This paper cites Shirley, Realistic Ray Tracing.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Shirley, Realistic Ray Tracing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.737653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ebda030f-1c7f-4ea7-8cf5-e472f398ab70 · outbound

This paper cites Calculation of potential flow about arbitrary bodies,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Calculation of potential flow about arbitrary bodies,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.725792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 145677a7-7355-4d47-a487-093d0962a831 · outbound

This paper cites The finite-difference time-domain (fd- td) method for electromagnetic scattering and interaction problems,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining The finite-difference time-domain (fd- td) method for electromagnetic scattering and interaction problems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.710768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 684b3b4c-6ecf-46f1-9f72-1741f8388b4f · outbound

This paper cites Xpatch 4: the next generation in high frequency electromagnetic modeling and simulation software,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Xpatch 4: the next generation in high frequency electromagnetic modeling and simulation software,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.694622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1dbab748-8646-46c9-8046-e2a8783e3cc3 · outbound

This paper cites Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:55:00.488296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dba04f58-a6c3-4bf5-9c45-b6da85aede39 · outbound

This paper cites Symmetric models for radar response modeling,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Symmetric models for radar response modeling,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.680400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 96b85932-b7a9-4ba9-8b32-3111789e9c4b · outbound

This paper cites Deep stochastic radar models,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Deep stochastic radar models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.664404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd7e4cf4-5b72-4487-b0a6-8f0e9c48d007 · outbound

This paper cites Learning to simulate complex physics with graph networks,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Learning to simulate complex physics with graph networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.647547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0a3238c7-2ba8-4b97-ba3a-e90cbf9f0cf3 · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Rethinking the Inception Architecture for Computer Vision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.134832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e20a61b-b0ab-40e0-aad0-ee7f45d36c03 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.139654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 593c78cd-9838-4a4c-bead-e3a7d0a9d068 · outbound

This paper cites Deep stochastic radar models,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Deep stochastic radar models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.632856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d2afa728-113c-4ff4-bad2-63b056840b03 · outbound

This paper cites Equidistant and uniform data augmentation for 3d objects,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Equidistant and uniform data augmentation for 3d objects,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.617646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35fcb735-c7b7-4cb1-9cd2-cef30343b4c1 · outbound

This paper cites Meshnet: Mesh neural network for 3d shape representation,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Meshnet: Mesh neural network for 3d shape representation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.599353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9f16dab7-1e14-4205-9b3b-1a4f3e213f74 · outbound

This paper cites LRM: Large Reconstruction Model for Single Image to 3D.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining LRM: Large Reconstruction Model for Single Image to 3D

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.157472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a25eab6-4e51-48df-a505-005f74947674 · outbound

This paper cites MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:55:00.405930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5c013630-54ae-4dfd-8de3-a56d348b1169 · outbound

This paper cites MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.166087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 987e58bc-1e13-4fa9-b789-b33fd07ade8c · outbound

This paper cites Meshcnn: a network with an edge,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Meshcnn: a network with an edge,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.170213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a577564-3215-4af5-94a7-fa4ab086b5de · outbound

This paper cites Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.583573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 25ea22e1-5ffb-4cc5-9172-a665d00bfe59 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining DINOv2: Learning Robust Visual Features without Supervision

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.177726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a20ac8df-3391-4436-821b-c0d34eda0c92 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining ShapeNet: An Information-Rich 3D Model Repository

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.181727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff222b9e-19ac-4d33-b97b-49d722816c91 · outbound

This paper cites Meshwalker: Deep mesh understanding by random walks,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Meshwalker: Deep mesh understanding by random walks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.569538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8eade7c-20c3-4378-a067-1f96614bbe62 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Imagenet: A large-scale hierarchical image database,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.189956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b582ba-ad13-4908-be45-15760322fe17 · outbound

This paper cites Meshgpt - pytorch.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Meshgpt - pytorch

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.540951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e626d032-943c-4acf-a9c0-dd9ddbfa3752 · outbound

This paper cites Attention is all you need,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Attention is all you need,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.518609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89d372b9-bfc2-4033-b634-a7db94321ee8 · outbound

This paper cites Objaverse: A Universe of Annotated 3D Objects.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Objaverse: A Universe of Annotated 3D Objects

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T19:55:00.205269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10436a62-a50c-4488-8bbf-e7adce96b95f · outbound

This paper cites Blender - a 3d modelling and rendering package,.

Reducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining Blender - a 3d modelling and rendering package,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:55:00.502189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.