Paper Citation Record · LEDGER

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2003.04919.

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

pith.paper-citation-record.v1
2003.04919 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:49.634759Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

68
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c82db6c7-1c60-4055-961a-3d3ffc16c6b8 · inbound

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks cites this paper.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:49.634759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d8c65e-a65e-4d5d-893f-5b7e9ff2e586 · inbound

The BdryMat\'ern GP: Reliable incorporation of boundary information on irregular domains for Gaussian process modeling cites this paper.

The BdryMat\'ern GP: Reliable incorporation of boundary information on irregular domains for Gaussian process modeling Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T18:14:09.139352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca963908-ae53-493a-8ea2-7b3eb12fd006 · inbound

Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches cites this paper.

Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-03T18:15:38.989074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88153066-6170-492d-9fe2-9d36bedffae0 · inbound

Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework cites this paper.

Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T07:30:28.002900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d3613df-f249-47f7-b7bb-13b73c4fbe4e · inbound

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems cites this paper.

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-09T19:56:16.418166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d3c92f3-33ed-49c5-8336-d44245a11bf9 · inbound

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming cites this paper.

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-25T00:36:29.998252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5816a02a-bd5a-4f59-a370-6598eae44aca · inbound

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming cites this paper.

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:25:00.083514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1bb48b7c-6824-4e4f-bda8-82937dff1b99 · inbound

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming cites this paper.

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:56:56.941783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d7ad1887-66ec-413e-98b2-32752501e27b · inbound

A Neural Surrogate Approach for Simulating Natural Convection Problems cites this paper.

A Neural Surrogate Approach for Simulating Natural Convection Problems Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:10:07.373746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73403170-44a1-4898-95db-8877f677e7d0 · inbound

Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints cites this paper.

Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:04:28.602590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c85800d-0eda-4723-8bc0-95edd67c2d5e · inbound

Generalist AI Control: Towards Multi-purpose Adaptive Algorithms cites this paper.

Generalist AI Control: Towards Multi-purpose Adaptive Algorithms Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T05:35:08.446588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca7028c7-c5b8-4615-9b64-552c6a81087a · inbound

Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning cites this paper.

Shaping Wind-Tunnel Airflow for Unmanned Aerial Vehicles using Online Learning Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T20:08:41.448922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.