Paper Citation Record · LEDGER

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2504.20019.

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

pith.paper-citation-record.v1
2504.20019 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:42:34.253155Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:02:11.486234Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T21:05:04.043340Z

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy19
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 046c2682-c78a-4761-8ad1-d75ba7311666 · outbound

This paper cites Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Unav-sim: A visually realistic underwater robotics simulator and synthetic data-generation framework,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.544992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e2efb473-dc6e-42fc-b28a-a9c3dfab5a57 · outbound

This paper cites Deep sea underwater robotic exploration in the ice-covered arctic ocean with auvs,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Deep sea underwater robotic exploration in the ice-covered arctic ocean with auvs,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.533556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d40bd01e-611d-40cf-bca2-915c4822fe73 · outbound

This paper cites Visual tracking nonlinear model predictive control method for autonomous wind turbine inspection,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Visual tracking nonlinear model predictive control method for autonomous wind turbine inspection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.522534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c5acc9c-e373-482f-9bcd-28badafc14e3 · outbound

This paper cites Estimation of External Force Acting on Underwater Robots,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Estimation of External Force Acting on Underwater Robots,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.509854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 124ca721-98af-403d-86e2-4d13ff3f846d · outbound

This paper cites Physics-informed machine learning,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Physics-informed machine learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T05:42:34.190473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8bb0fb-db48-42cf-96ef-abe3e72ed9aa · outbound

This paper cites Safe Physics-Informed Machine Learning for Dynamics and Control.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Safe Physics-Informed Machine Learning for Dynamics and Control

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:42:34.194243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 891e46b1-ab26-4f99-aca1-55647ae08dcf · outbound

This paper cites Physics-Informed Neural Nets for Control of Dynamical Systems.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Physics-Informed Neural Nets for Control of Dynamical Systems

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:42:34.198513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41adbe99-8cef-4998-ba2f-74e286ed58b0 · outbound

This paper cites an unresolved cited work.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:42:34.491656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1c111d92-9caf-40e5-9390-0d81e9a67631 · outbound

This paper cites Empowering autonomous underwater vehicles using learning-based model predictive control with dynamic forgetting gaussian processes,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Empowering autonomous underwater vehicles using learning-based model predictive control with dynamic forgetting gaussian processes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.479455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ad47911-157b-4298-9850-a9fa2cba3bee · outbound

This paper cites Adaptive robust control integrated with gaussian processes for quadrotors: Enhanced accuracy, fault tolerance and anti-disturbance,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Adaptive robust control integrated with gaussian processes for quadrotors: Enhanced accuracy, fault tolerance and anti-disturbance,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.466348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c44ee34b-7108-44a4-973a-18a6c88e08fb · outbound

This paper cites A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.454145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e7f1c85-5785-4c2d-be66-97dd885006ee · outbound

This paper cites Ramp-net: A robust adaptive mpc for quadrotors via physics-informed neural network,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Ramp-net: A robust adaptive mpc for quadrotors via physics-informed neural network,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.441791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1bfb4d32-f83d-4010-b4e2-a0543b096241 · outbound

This paper cites Combining physics and deep learning to learn continuous-time dynamics models,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Combining physics and deep learning to learn continuous-time dynamics models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.430437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 78876220-66a1-4425-ae4c-c766b12dfff7 · outbound

This paper cites Development and Implementation of Physics-Informed Neural ODE for Dynamics Model- ing of a Fixed-Wing Aircraft Under Icing/Fault,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Development and Implementation of Physics-Informed Neural ODE for Dynamics Model- ing of a Fixed-Wing Aircraft Under Icing/Fault,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.418337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ee864ac-d537-423b-a34c-3fba37aa5936 · outbound

This paper cites Neural ordinary differential equations,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Neural ordinary differential equations,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T05:42:34.225325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ab41418-67d8-40f4-b3f8-69a55430d1a1 · outbound

This paper cites Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics- Informed Neural Network,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics- Informed Neural Network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.400625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0daa9087-5d4b-4885-a40c-9a0620085d75 · outbound

This paper cites Sim-to- Real of Soft Robots with Learned Residual Physics,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Sim-to- Real of Soft Robots with Learned Residual Physics,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.389662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5c4c2725-d22c-43e6-91c8-d171af9df11a · outbound

This paper cites Domain-decoupled Physics-informed Neural Networks with Closed- form Gradients for Fast Model Learning of Dynamical Systems,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Domain-decoupled Physics-informed Neural Networks with Closed- form Gradients for Fast Model Learning of Dynamical Systems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.378938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1eed7a4-3675-45cf-8b1f-36a66677757c · outbound

This paper cites Physics-Informed Neural Networks with Skip Connections for Model- ing and Control of Gas-Lifted Oil Wells,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Physics-Informed Neural Networks with Skip Connections for Model- ing and Control of Gas-Lifted Oil Wells,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.366890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 22bfcc22-4ae1-42de-8ab2-c433b6741ad4 · outbound

This paper cites ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.353815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e233c72b-42fa-4880-9f7e-08e5cee561f3 · outbound

This paper cites Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.341800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09f98d3c-dc5e-43d2-a937-6dbab081a448 · outbound

This paper cites Handbook of marine craft hydrodynamics and motion control,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Handbook of marine craft hydrodynamics and motion control,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.329721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 23b3f691-1db7-4210-b55f-a4e7af07ea48 · outbound

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

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.317700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f35a46e1-cdb4-47bd-9385-c1c737464c8f · outbound

This paper cites Layer Normalization,.

Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control Layer Normalization,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:42:34.304452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 47d706f8-9de7-4b21-ab23-c90e07e5d8e1 · inbound

SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping cites this paper.

SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping Modelling of Underwater Vehicles using Physics-Informed Neural Networks with Control

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:05:04.044895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.