Paper Citation Record · LEDGER

Theory Foundation of Physics-Enhanced Residual Learning

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.00348.

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

pith.paper-citation-record.v1
2509.00348 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:49:52.327638Z

measured 15 of 15 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

15 of 15 outbound references displayed

verified exact4
verified fuzzy6
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 149b66e3-ea3a-4e91-8db1-158bff9c5da4 · outbound

This paper cites sup f ∈F nX i=1 σiℓ(zi(f )) # ≤ L E.

Theory Foundation of Physics-Enhanced Residual Learning sup f ∈F nX i=1 σiℓ(zi(f )) # ≤ L E

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:53.190021Z

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 a742f198-80a7-4984-b64e-2c570c48373e · outbound

This paper cites Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies.

Theory Foundation of Physics-Enhanced Residual Learning Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:49:53.011026Z

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 d79ed14f-5c53-4a85-8097-4333800ed33f · outbound

This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Theory Foundation of Physics-Enhanced Residual Learning Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T13:49:51.665463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3b22a89-e710-40d6-b690-48be8028e73c · outbound

This paper cites An overview of gradient descent optimization algorithms.

Theory Foundation of Physics-Enhanced Residual Learning An overview of gradient descent optimization algorithms

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T13:49:51.752456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97a76026-027c-409b-abf0-335bcd38f600 · outbound

This paper cites Generalization and Estimation Error Bounds for Model-based Neural Networks.

Theory Foundation of Physics-Enhanced Residual Learning Generalization and Estimation Error Bounds for Model-based Neural Networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:49:52.831656Z

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 8d398fee-b95a-4bfa-8d0e-80f51e8e7282 · outbound

This paper cites A Unified Longitudinal Trajectory Dataset for Automated Vehicle.

Theory Foundation of Physics-Enhanced Residual Learning A Unified Longitudinal Trajectory Dataset for Automated Vehicle

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:49:52.526485Z

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 6743e709-a541-4634-b6ca-df0ac1fc9fb9 · outbound

This paper cites Deep-gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning.

Theory Foundation of Physics-Enhanced Residual Learning Deep-gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning

Reference 1986

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:53.646960Z

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 40832e63-27e7-4e0b-b717-45f9699acfaf · outbound

This paper cites an unresolved cited work.

Theory Foundation of Physics-Enhanced Residual Learning Unresolved cited work

Reference 2003

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:49:53.343209Z

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 b56f6352-4c00-4391-ab87-725bc77d3182 · outbound

This paper cites Physics-informed deep reinforce- ment learning-based integrated two-dimensional car-following control strategy for connected automated vehicles.

Theory Foundation of Physics-Enhanced Residual Learning Physics-informed deep reinforce- ment learning-based integrated two-dimensional car-following control strategy for connected automated vehicles

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:53.502361Z

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 598fbc7f-9b7f-4132-b565-f6af8ce607ba · outbound

This paper cites ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills.

Theory Foundation of Physics-Enhanced Residual Learning ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-05T13:49:51.407266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67e578cf-ff4c-47db-a438-d463455540b6 · outbound

This paper cites Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning.

Theory Foundation of Physics-Enhanced Residual Learning Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:49:52.667025Z

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 4362f94e-648d-4c6a-81be-7b39dbe8915a · outbound

This paper cites Learning both weights and connections for efficient neural network.

Theory Foundation of Physics-Enhanced Residual Learning Learning both weights and connections for efficient neural network

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:53.933439Z

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 49003290-795e-408f-9a3b-f8f6b89502d4 · outbound

This paper cites Constrained physical-statistics models for dynamical system identification and prediction.

Theory Foundation of Physics-Enhanced Residual Learning Constrained physical-statistics models for dynamical system identification and prediction

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:54.062017Z

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 b98b19e2-39f7-4ef2-998c-ceaba18d19a2 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Theory Foundation of Physics-Enhanced Residual Learning Towards A Rigorous Science of Interpretable Machine Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T13:49:51.214139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3763eca-6e3f-42df-be5c-fe6bf0bc5874 · outbound

This paper cites Short-term traffic flow prediction with lstm recurrent neural network.

Theory Foundation of Physics-Enhanced Residual Learning Short-term traffic flow prediction with lstm recurrent neural network

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:49:53.818767Z

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.