Paper Citation Record · LEDGER

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2506.12742.

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

pith.paper-citation-record.v1
2506.12742 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:53:34.977888Z

measured 40 of 40 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:29:17.717612Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy25
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 98dfdf4b-8946-47d0-ae0c-f29ea4150a56 · outbound

This paper cites Planning algorithms,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Planning algorithms,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:41.407816Z

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 4d991e03-f4ab-400c-9b3c-d14ed6aa0a22 · outbound

This paper cites Sampling-based algorithms for optimal motion planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Sampling-based algorithms for optimal motion planning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:30.674695Z

Source-reported events for the cited work

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Observation ddd1800e-ac2e-48a2-8230-f65fb93ccd47 · outbound

This paper cites Rapidly-exploring ran- dom trees: Progress and prospects,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Rapidly-exploring ran- dom trees: Progress and prospects,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:41.134487Z

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 b0d7086d-c176-43c2-8b70-789c6f8bdabb · outbound

This paper cites Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:40.845832Z

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 845d5ff9-e748-41e5-9714-c139cadf8fdd · outbound

This paper cites Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions,

Reference 5

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Source-reported events for the cited work

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Observation 2cb5c500-1eee-4bf8-8dac-3f48fff6eb06 · outbound

This paper cites Motion planning networks,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Motion planning networks,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:31.051175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad6e3458-85de-4d7d-a033-ffc1e9830ea7 · outbound

This paper cites Motion planning networks: Bridging the gap between learning-based and classical motion planners,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Motion planning networks: Bridging the gap between learning-based and classical motion planners,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:31.215848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eeb98bb-b7ee-4e51-a5fe-840744fb13ae · outbound

This paper cites Learning sampling dis- tributions for robot motion planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Learning sampling dis- tributions for robot motion planning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:40.579393Z

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 b5ec0ce7-f29e-4e59-afe3-574d52cabd18 · outbound

This paper cites Deeply informed neural sampling for robot motion planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Deeply informed neural sampling for robot motion planning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:40.318456Z

Source-reported events for the cited work

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Observation 4626f077-ce14-41c7-a4f4-588581cf5c58 · outbound

This paper cites Lego: Leveraging experience in roadmap generation for sampling-based planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Lego: Leveraging experience in roadmap generation for sampling-based planning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:40.052614Z

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 12a6aef8-6472-4eeb-96b7-c1bab0fc4646 · outbound

This paper cites Differentiable spatial plan- ning using transformers,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Differentiable spatial plan- ning using transformers,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:39.838230Z

Source-reported events for the cited work

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Observation 28097361-a948-4a0b-836c-b2cf58249e98 · outbound

This paper cites NTFields: Neural time fields for physics- informed robot motion planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments NTFields: Neural time fields for physics- informed robot motion planning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:39.625722Z

Source-reported events for the cited work

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Observation 69dd5a6d-8c65-46ae-90cf-3c15e5cfdb68 · outbound

This paper cites Progressive Learning for Physics-informed Neural Motion Planning.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Progressive Learning for Physics-informed Neural Motion Planning

Reference 13

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Source-reported events for the cited work

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Observation 0d777a2c-5620-4e7f-a452-d8191793296b · outbound

This paper cites Pc-planner: Physics-constrained self-supervised learning for robust neural motion planning with shape-aware distance function,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Pc-planner: Physics-constrained self-supervised learning for robust neural motion planning with shape-aware distance function,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:39.409011Z

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 f62c0d3a-05fb-4ad5-8687-6ecf05965e2f · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:31.982689Z

Source-reported events for the cited work

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Observation a65aefa5-5fd4-4b6a-bc74-ddf5bbd5e24e · outbound

This paper cites Physics-informed machine learning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Physics-informed machine learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:32.093624Z

Source-reported events for the cited work

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Observation 7e5554b2-838e-42f1-84c2-f89162564638 · outbound

This paper cites Viscosity solutions of hamilton- jacobi equations,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Viscosity solutions of hamilton- jacobi equations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:39.175190Z

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 6def9925-79b1-4ff1-86b1-154e3e58ea37 · outbound

This paper cites Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:32.360402Z

Source-reported events for the cited work

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Observation fe8ad702-8fd2-43c9-ae70-80555aee3227 · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Characterizing possible failure modes in physics-informed neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:38.876044Z

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 8205c942-151e-4a89-9be5-1fa9a3be7460 · outbound

This paper cites Finite basis physics- informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Finite basis physics- informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:38.634898Z

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 aad1cf3c-a41c-4541-96bb-f7583afd5637 · outbound

This paper cites Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:38.426620Z

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 e5aefc52-11be-4bfa-93c3-24c29254f3a5 · outbound

This paper cites A fast marching level set method for monotonically advancing fronts,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments A fast marching level set method for monotonically advancing fronts,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:38.158421Z

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 e428ed67-6fca-4e7e-a5b8-e0892a8e9301 · outbound

This paper cites Direct and indirect methods for trajectory optimization,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Direct and indirect methods for trajectory optimization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:37.893682Z

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 1390ac04-e211-4a45-b478-83d264546fdc · outbound

This paper cites Path-constrained trajectory opti- mization using sparse sequential quadratic programming,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Path-constrained trajectory opti- mization using sparse sequential quadratic programming,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:37.690351Z

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 5bdd0fc6-33ce-49f6-8479-8ad1994133e3 · outbound

This paper cites Chomp: Covariant hamiltonian optimization for motion planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Chomp: Covariant hamiltonian optimization for motion planning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:37.413866Z

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 0368cdfe-2317-4962-8a73-7db6b7b01896 · outbound

This paper cites Value iter- ation networks,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Value iter- ation networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:37.143786Z

Source-reported events for the cited work

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Observation 8ea84816-c812-49bf-a814-af867a26c8a7 · outbound

This paper cites Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,

Reference 27

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Source-reported events for the cited work

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Observation 0818a274-cb80-4fbb-9929-1699e0f9b4bc · outbound

This paper cites Universal planning networks: Learning generalizable representations for visuo- motor control,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Universal planning networks: Learning generalizable representations for visuo- motor control,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:36.854874Z

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 e39b5b91-afc7-40a5-b30f-86262d7e2462 · outbound

This paper cites Control transformer: robot navigation in unknown environments through prm-guided return-conditioned sequence modeling,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Control transformer: robot navigation in unknown environments through prm-guided return-conditioned sequence modeling,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:36.579865Z

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 ec380e86-85d0-4224-966a-740b043eba36 · outbound

This paper cites Physics-informed Neural Motion Planning on Constraint Manifolds.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Physics-informed Neural Motion Planning on Constraint Manifolds

Reference 30

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Source-reported events for the cited work

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Observation ab13759b-7db0-464b-a64d-5748bc4db730 · outbound

This paper cites Tensorf: Tensorial ra- diance fields,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Tensorf: Tensorial ra- diance fields,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:36.417001Z

Source-reported events for the cited work

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Observation d2fbb795-916a-4838-86c8-676edab684b2 · outbound

This paper cites Instant neural graphics primitives with a multiresolution hash encoding,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Instant neural graphics primitives with a multiresolution hash encoding,

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e55beff6-a6b2-49ae-abc4-39cfeb5233a5 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Nerf: Representing scenes as neural radiance fields for view synthesis,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:36.165297Z

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 996114e1-0c5a-4e8c-a1ac-f38b93af63d0 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimen- sional domains,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Fourier features let networks learn high frequency functions in low dimen- sional domains,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:35.946591Z

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 d473662c-0a79-42b6-8a8a-9d44d1fc0646 · outbound

This paper cites Implicit neural representations with periodic activation functions,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Implicit neural representations with periodic activation functions,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:35.745067Z

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 e2b976dd-6082-4aa4-ba59-ca671ebc6098 · outbound

This paper cites iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:34.651343Z

Source-reported events for the cited work

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Observation 59452347-c024-48cc-8923-3cfcaac43914 · outbound

This paper cites RRT-connect: An efficient approach to single-query path planning,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments RRT-connect: An efficient approach to single-query path planning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:35.512630Z

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 1cb49ddd-3e9a-40ad-ab64-b44573aa5b42 · outbound

This paper cites Path planning using lazy prm,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments Path planning using lazy prm,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T00:53:34.857356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d3b7247-ccaf-49f2-8ad3-06b4e7225096 · outbound

This paper cites GEASI: Geodesic-based earliest activation sites identification in cardiac models,.

Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments GEASI: Geodesic-based earliest activation sites identification in cardiac models,

Reference 39

Resolution: verified exact; doi, observed 2026-08-07T00:53:35.225974Z

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

Observation 1f7a6bee-c769-451a-892d-5daece239d71 · inbound

Mollified Value Learning cites this paper.

Mollified Value Learning Physics-informed Neural Motion Planning via Domain Decomposition in Large Environments

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T20:29:17.717612Z

Source-reported events for the cited work

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