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Paper Citation Record · LEDGER

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule

As of 9 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2603.28791.

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

pith.paper-citation-record.v1
2603.28791 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:47:09.291817Z

measured 13 of 13 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 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

13 of 13 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved11
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39aad6db-c86c-4b93-a3b3-e54f03f05b4d · outbound

This paper cites Machine Learning in Aerodynamic Shape Optimization.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Machine Learning in Aerodynamic Shape Optimization

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.023766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bf5bdb0f-9c19-44d9-a52d-6a10b486bfd5 · outbound

This paper cites an unresolved cited work.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.263489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:09.263489Z digest=sha256:e4fe1436a43ceaa860b84d3bc02068cc902ec1a04061a461942c7134c37036b0

Observation 6e2c8245-573a-4d2a-82d4-ceafc31a3d64 · outbound

This paper cites Reconstruction of irregular flow dynamics around two square cylinders from sparse measurements using a data-driven algorithm.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Reconstruction of irregular flow dynamics around two square cylinders from sparse measurements using a data-driven algorithm

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.270426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:09.270426Z digest=sha256:21ae32275f6c791fa7acf5fb294422a0a69140a6c977bfe3dc2a0f9e2c0ec34f

Observation e6131349-930b-427f-81a9-e52b0880b1f0 · outbound

This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 1845

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.277877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:09.277877Z digest=sha256:14bd7153b52a94564adc31a94f1b53e6cc4339b54c478ae4f7b324e979abc3f5

Observation 429e96a5-64c0-4aa8-b3a9-1b75242cc811 · outbound

This paper cites Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio

Reference 1979

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.284950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:09.284950Z digest=sha256:b7cc91b10bb22b009105a3d63fee52c6a861706503fba8d5233fad3d9769aa47

Observation 20ac4100-aa40-4665-a130-287155623545 · outbound

This paper cites Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow

Reference 2003

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.363659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.363659Z digest=sha256:d56695ba9ab57b0bafad80a1fd81d48a6dad73d509dc3618fca146d3edbeb09e

Observation 58f7384f-6b31-459a-96a8-a69add5c5366 · outbound

This paper cites an unresolved cited work.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Unresolved cited work

Reference 2010

Resolution
verified exact
doi, observed 2026-08-02T17:48:31.334378Z

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.

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Observation 6f14a7d7-4e29-43c5-a9a1-6025b4e9ea56 · outbound

This paper cites Neural Operator: Learning Maps Between Function Spaces.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Neural Operator: Learning Maps Between Function Spaces

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.876587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.876587Z digest=sha256:7e5bcb51464a02e9142632cb110cf540826273dd920de971e412c9ce108c2737

Observation 47f171c9-aa2f-471c-83c0-5c39f19811dc · outbound

This paper cites Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Machine Learning Methods for the Design and Operation of Liquid Rocket Engines -- Research Activities at the DLR Institute of Space Propulsion

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:09.291817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:09.291817Z digest=sha256:313dea2ca35084e82f555b88b22d90e04b6a4b30ef756c2e0ebb84ab0e7728e5

Observation 4bf06231-c4b5-4c97-8c03-4c8d5b73015b · outbound

This paper cites an unresolved cited work.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Unresolved cited work

Reference 2020

Resolution
verified exact
doi, observed 2026-08-02T17:48:31.190330Z

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.

source=pdf_text observed=2026-08-02T17:47:09.190141Z digest=sha256:e7fcd56e41909c34e6e7d021641f1d3518056af9a6809db6dcaa1fd97784fe82

Observation d493239a-af33-4054-92c6-025fa0d0d109 · outbound

This paper cites Thomas Kipf and Max Welling.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Thomas Kipf and Max Welling

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.772509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.772509Z digest=sha256:ad9e076a299a8b4800ddf18c4404d712d1ce21827de392d80f3089f638eb134f

Observation 2e6cbf6d-e2c6-40e4-a456-005f5f3e878a · outbound

This paper cites Aerothermodynamic Simulators for Rocket Design using Neural Fields.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Aerothermodynamic Simulators for Rocket Design using Neural Fields

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.429160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.429160Z digest=sha256:1a3171def659d4be4a6da4f07bf035353f9eb0e7fe24119be8c0ada57470185a

Observation 4dc40718-aa3b-4f67-b0cd-c23d05fb9608 · outbound

This paper cites Mathematical Foundations of Geometric Deep Learning.

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule Mathematical Foundations of Geometric Deep Learning

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T17:47:08.537271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T17:47:08.537271Z digest=sha256:12fde248139b7d42f303bbcdebf47426aaffcf975af3d801e9948f35c3639b70

Pith citing papers

No inbound Pith citation observations are available.