Paper Citation Record · LEDGER

Multiple Physics Pretraining for Physical Surrogate Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2310.02994.

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

pith.paper-citation-record.v1
2310.02994 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:14:59.603073Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:24:00.997379Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b47a1a50-dc4e-497d-b436-7f5a863bc022 · inbound

HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture cites this paper.

HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture Multiple Physics Pretraining for Physical Surrogate Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T00:14:59.603073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 976070ee-ee96-44c8-aa5a-559bee5d0e23 · inbound

Machine learning for modelling unstructured grid data in computational physics: a review cites this paper.

Machine learning for modelling unstructured grid data in computational physics: a review Multiple Physics Pretraining for Physical Surrogate Models

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-07T21:49:41.502178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acad507e-5144-41d1-b09f-2d99d78fe08d · inbound

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit cites this paper.

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit Multiple Physics Pretraining for Physical Surrogate Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:09:21.583016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4a5b7ad-d48e-4a38-90fd-240dba5555d8 · inbound

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations cites this paper.

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations Multiple Physics Pretraining for Physical Surrogate Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T05:33:52.886379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfcd53c6-3507-4b2a-bd5d-12b292bd6593 · inbound

Towards a Physics Foundation Model cites this paper.

Towards a Physics Foundation Model Multiple Physics Pretraining for Physical Surrogate Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T16:32:58.698197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1f212b1-0a2a-44a8-af4f-9ad4eda950e8 · inbound

Flow marching for a generative PDE foundation model cites this paper.

Flow marching for a generative PDE foundation model Multiple Physics Pretraining for Physical Surrogate Models

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:51:25.497762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 016447db-68ac-4382-8178-26cd98dcdeef · inbound

Latent Generative Solvers for Generalizable Long-Term Physics Simulation cites this paper.

Latent Generative Solvers for Generalizable Long-Term Physics Simulation Multiple Physics Pretraining for Physical Surrogate Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T05:22:22.601086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1333579e-5687-44a1-9639-4adcf4519cdb · inbound

A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems cites this paper.

A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems Multiple Physics Pretraining for Physical Surrogate Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:33:17.079279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ca14fda8-dbb2-4da9-8020-91e29d619f92 · inbound

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting cites this paper.

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting Multiple Physics Pretraining for Physical Surrogate Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:14:46.535576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6980c57a-9550-4b0b-9172-cf20860cd8a6 · inbound

AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training cites this paper.

AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training Multiple Physics Pretraining for Physical Surrogate Models

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-20T21:03:46.668577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b99f8e7e-6c0d-4fdc-bbbc-8bd8e3f239fc · inbound

Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning cites this paper.

Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning Multiple Physics Pretraining for Physical Surrogate Models

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:34:42.686782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc88f2aa-2638-4a60-9625-f80cd6ee3e34 · inbound

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers cites this paper.

Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers Multiple Physics Pretraining for Physical Surrogate Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.998925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7f40782b-0c2c-4a70-8619-c33294da6b45 · inbound

Neural operator discovery from heterogeneous trajectories cites this paper.

Neural operator discovery from heterogeneous trajectories Multiple Physics Pretraining for Physical Surrogate Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T23:47:28.083363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 304be9c2-c1c4-407b-808b-a3698cb3b744 · inbound

Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors cites this paper.

Materials Behavior as Mechanism Ensembles: A Probabilistic Framework for Emergent Behaviors Multiple Physics Pretraining for Physical Surrogate Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-30T10:34:45.318675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fca2db98-1d86-4e25-84b3-9c883a55a259 · inbound

Foundation Models for Astrophysics cites this paper.

Foundation Models for Astrophysics Multiple Physics Pretraining for Physical Surrogate Models

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-04T04:31:48.910538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a267371-fa31-4edd-806b-2deda284579d · inbound

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning cites this paper.

TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning Multiple Physics Pretraining for Physical Surrogate Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T00:16:45.680684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.