Paper Citation Record · LEDGER

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2502.19397.

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

pith.paper-citation-record.v1
2502.19397 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:54:20.734400Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:27:34.851414Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T05:10:54.401477Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b74eb94-42c0-481f-b371-d699dfd782f8 · outbound

This paper cites M.; Waage, P.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations M.; Waage, P

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:21.153701Z

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Observation e262fe0b-d561-4c07-ab67-107f23316ab8 · outbound

This paper cites Mathematical models of chemical reactions: theory and applications of deterministic and stochastic models; Manchester University Press, 1989.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Mathematical models of chemical reactions: theory and applications of deterministic and stochastic models; Manchester University Press, 1989

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:21.142712Z

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Observation ba012dca-7165-453a-bac6-6338436ff652 · outbound

This paper cites A.; England, J.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations A.; England, J

Reference 3

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Observation 1fbd82d3-ec39-486a-a686-2005ef0873c7 · outbound

This paper cites F.; Pathirana, D.; Fröhlich, F.; Hasenauer, J.; Banga, J.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations F.; Pathirana, D.; Fröhlich, F.; Hasenauer, J.; Banga, J

Reference 4

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Observation 8c6d2a8b-62ae-4518-83b1-ed458cc9f117 · outbound

This paper cites T.; Weindl, D.; Hasenauer, J.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations T.; Weindl, D.; Hasenauer, J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:21.110192Z

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Observation b4e5a148-3906-455b-9cc3-e1a5c5ea4e02 · outbound

This paper cites Linking data to models: Data regression.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Linking data to models: Data regression

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:21.099486Z

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Observation ead6d224-b5b4-45da-8cb9-7eeaaadce7a7 · outbound

This paper cites Competition for catalytic resources alters biological network dynamics.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Competition for catalytic resources alters biological network dynamics

Reference 7

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Observation 2ec48ebb-1b72-49ea-8a1f-0c1306b1a5b2 · outbound

This paper cites an unresolved cited work.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 8

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Observation 8c83eb00-7f35-4808-80ce-a99a26cc8c13 · outbound

This paper cites T.; Rubanova, Y.; Bettencourt, J.; Duvenaud, D.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations T.; Rubanova, Y.; Bettencourt, J.; Duvenaud, D

Reference 9

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Observation 7d2d1b98-90d8-445c-9259-6b47b33448d3 · outbound

This paper cites Deep learning.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Deep learning

Reference 10

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Observation d06b60c7-a13f-4de0-881d-57e5c9a94201 · outbound

This paper cites V.; Azevedo, P.; Cardoso, V.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations V.; Azevedo, P.; Cardoso, V

Reference 11

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Observation 1e36366e-0ad1-4b30-8ce4-94f3476ca840 · outbound

This paper cites D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; others Language models are few-shot learners.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; others Language models are few-shot learners

Reference 12

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Observation 2ccbbc00-b116-40bc-83b4-6f814a31f78b · outbound

This paper cites E.; Bachrach, Y.; Huck, W.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations E.; Bachrach, Y.; Huck, W

Reference 13

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Observation 79fac0f8-9008-4679-9dee-3ab7c0a227ab · outbound

This paper cites nature 2021, 596, 583--589.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations nature 2021, 596, 583--589

Reference 14

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Observation 140b5bc9-0c46-45ca-bf1a-a7a75beae6d0 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Multilayer feedforward networks are universal approximators

Reference 15

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Observation e7049d53-5605-46ce-a6d1-a531927cfc29 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Deep Residual Learning for Image Recognition

Reference 16

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Observation 2814c02c-dd14-47d8-a968-b6e0f3ce4c01 · outbound

This paper cites Density estimation using Real NVP.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Density estimation using Real NVP

Reference 17

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Observation 9e91da3b-06f9-46f8-bb35-5e7b9426b310 · outbound

This paper cites Neural Controlled Differential Equations for Irregular Time Series.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Neural Controlled Differential Equations for Irregular Time Series

Reference 18

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Observation 05591eb9-b305-4708-8cde-cff5c505c169 · outbound

This paper cites Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:20.981552Z

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Observation bdae733d-45a0-43d9-b005-69e79e29414e · outbound

This paper cites ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:20.970616Z

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Observation 52af010d-8922-45ac-99bf-57f540293bea · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Universal Differential Equations for Scientific Machine Learning

Reference 21

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Observation 58868d7d-74e1-4e46-b616-f658a3ec4a17 · outbound

This paper cites R.; Runikhina, S.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations R.; Runikhina, S

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:20.960308Z

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Observation c2fe9293-4fa9-4227-b323-8d3c0f8b6f20 · outbound

This paper cites A.; Essex, C.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations A.; Essex, C

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:20.950546Z

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Observation c0a3106c-72db-4b0a-b68f-240dbbc677b2 · outbound

This paper cites Singly diagonally implicit Runge--Kutta methods with an explicit first stage.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Singly diagonally implicit Runge--Kutta methods with an explicit first stage

Reference 24

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Observation 419c85dc-0e74-47ee-901e-5d9b1e2ef611 · outbound

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 25

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Observation 353e0404-fd70-407b-8179-cb00e5e4db17 · outbound

This paper cites E quinox: neural networks in JAX via callable P y T rees and filtered transformations.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations E quinox: neural networks in JAX via callable P y T rees and filtered transformations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:54:20.920968Z

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Observation a5aa5064-30ad-4e1e-b207-8a860197ef10 · outbound

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 27

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Observation 4438ce77-f54e-4c2e-a2f7-9ea3318794ce · outbound

This paper cites Distilling Free-Form Natural Laws from Experimental Data.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Distilling Free-Form Natural Laws from Experimental Data

Reference 28

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Observation 627fde12-2780-4f10-9150-4110089bea9a · outbound

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 29

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Observation 1ba09363-16d3-482a-9a27-66c90d60d38b · outbound

This paper cites Long short-term memory.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Long short-term memory

Reference 30

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Observation 6093cf7a-0f5c-4e96-b382-1f562a51758c · outbound

This paper cites A Tutorial on Chemical Reaction Network Dynamics.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations A Tutorial on Chemical Reaction Network Dynamics

Reference 31

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Observation 674dfe36-5d4a-4840-bc73-34f3f2a535e1 · outbound

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 32

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 33

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Observation 4c85ebd7-d663-477c-a099-02ad651f1dd5 · outbound

This paper cites Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations

Reference 34

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Observation e1300499-bcbf-4b6d-838e-9e0cc88be0c5 · outbound

This paper cites O n N eural D ifferential E quations.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations O n N eural D ifferential E quations

Reference 35

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Observation 9d7cbc55-a131-4e08-aa36-fb8b4cddc142 · outbound

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Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations Unresolved cited work

Reference 36

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Observation 8ef536ea-2501-4cbf-83b0-964f4aaa4e7b · outbound

This paper cites ODEFormer: Symbolic Regression of Dynamical Systems with Transformers.

Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations ODEFormer: Symbolic Regression of Dynamical Systems with Transformers

Reference 37

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Pith citing papers

Observation 5a98b19f-4e72-4c2a-a092-aac273f5069d · inbound

Calibrated Physics-Informed Uncertainty Quantification cites this paper.

Calibrated Physics-Informed Uncertainty Quantification Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations

Reference 17

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Observation 59b7c8b6-4668-4c87-aebf-da7ccc13fd34 · inbound

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling cites this paper.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations

Reference 11

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Source-reported events for the cited work

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