Paper Citation Record · LEDGER

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2411.15919.

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

pith.paper-citation-record.v1
2411.15919 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:50:50.404388Z

measured 16 of 16 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 2026-08-04T22:39:13.716161Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy10
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation b4925eea-28d2-4d5f-9353-c3471df271a1 · outbound

This paper cites Dimensionally consistent learning with buckingham pi.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Dimensionally consistent learning with buckingham pi

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.555948Z

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 b1f0556b-8d6f-48db-9327-b8b3fdcda94b · outbound

This paper cites Buckingham– π–theorem, 2021.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Buckingham– π–theorem, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.547098Z

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 48863ae8-b952-4f39-93e9-6a48fb924a2a · outbound

This paper cites On physically similar systems; illustrations of the use of dimensional equations.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis On physically similar systems; illustrations of the use of dimensional equations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.538059Z

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 2641b4c3-6b84-491c-a4fe-a2343dbb7278 · outbound

This paper cites An alternate means to form non-dimensional products in dimensional analysis.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis An alternate means to form non-dimensional products in dimensional analysis

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.529020Z

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 41782bbb-deaf-48dc-bf76-08ae0fd23368 · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T13:50:50.371415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 781143cc-5e5e-4dd8-a039-d7a59c4124e6 · outbound

This paper cites Kaptanoglu, Brian M.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Kaptanoglu, Brian M

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.519369Z

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 69aae95e-2105-42ee-ac12-3005ed585bc0 · outbound

This paper cites A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.510353Z

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 c7cf9e9a-60fb-4580-b830-c36f27321fbb · outbound

This paper cites Partial differential equations discovery with epde framework: application for real and synthetic data.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Partial differential equations discovery with epde framework: application for real and synthetic data

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.501252Z

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 3f8d0cdd-dd5b-40db-97a2-83f13c774e78 · outbound

This paper cites Universal physics-informed neural networks: Symbolic differential operator discovery with sparse data.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Universal physics-informed neural networks: Symbolic differential operator discovery with sparse data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.491178Z

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 e1169af7-fbc6-4858-83f2-42d3d48afb20 · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Universal Differential Equations for Scientific Machine Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:50:50.388312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b554fb7e-5d1f-4d99-b1f3-08f6cff96aff · outbound

This paper cites Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.481247Z

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 c7e879a0-38b2-43b5-9ed6-812d14b8beda · outbound

This paper cites Ai feynman: A physics-inspired method for symbolic regression.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Ai feynman: A physics-inspired method for symbolic regression

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:50:50.394872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bd26b9d-48ac-40c3-91a3-917f07cf7e19 · outbound

This paper cites SymbolicGPT: A Generative Transformer Model for Symbolic Regression.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis SymbolicGPT: A Generative Transformer Model for Symbolic Regression

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T13:50:50.397735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1930a4f3-1efb-4320-a5d4-15e7c84d7367 · outbound

This paper cites Symbolic Regression is NP-hard.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis Symbolic Regression is NP-hard

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T13:50:50.401186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af47cd43-9c61-48c2-8fb0-5716bf7032ab · outbound

This paper cites White and H.

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis White and H

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:50:50.463234Z

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 3d0bb52a-9dcb-4cce-b7be-462c93727a73 · inbound

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation cites this paper.

IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:39:13.858338Z

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.