Paper Citation Record · LEDGER

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling

As of 10 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2510.22104.

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

pith.paper-citation-record.v1
2510.22104 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-18T05:07:45.633258Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

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Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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verified fuzzy19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e585606-2ac9-4af0-bc29-7c23d9167faf · outbound

This paper cites A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:10:54.406948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8bf88bc7-525b-4bce-a7f8-43419b3f2e83 · outbound

This paper cites First-order differential equations in chemistry.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling First-order differential equations in chemistry

Reference 2

Resolution: verified exact; doi, observed 2026-05-18T05:10:54.097186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbd2e047-f3c4-4da1-85ea-0bcda94b0e86 · outbound

This paper cites Learning transmission dynamics modelling of covid-19 using comomodels.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Learning transmission dynamics modelling of covid-19 using comomodels

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.227030Z

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Observation ebc93742-51f1-47a8-b991-2a02ebe4aaa5 · outbound

This paper cites an unresolved cited work.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Unresolved cited work

Reference 4

Resolution: verified exact; doi, observed 2026-05-18T05:10:54.094362Z

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Observation ff308dd1-796b-4f85-a887-6c6d9b3d1ecf · outbound

This paper cites On generalized residual network for deep learning of unknown dynamical systems.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling On generalized residual network for deep learning of unknown dynamical systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.222206Z

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Observation 23c7cbcf-52c1-4fce-8ce3-8b7daa95f177 · outbound

This paper cites Neural ordinary differential equations.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Neural ordinary differential equations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.224361Z

Source-reported events for the cited work

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Observation c558f42c-d701-4f94-90f3-b2120988d500 · outbound

This paper cites Feasibility study of neural ode and dae modules for power system dynamic component modeling.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Feasibility study of neural ode and dae modules for power system dynamic component modeling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.253620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cb65c323-28a8-4fcf-ad4e-8ce82e2231be · outbound

This paper cites Learning power system dynamics with noisy data using neural ordinary differential equations.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Learning power system dynamics with noisy data using neural ordinary differential equations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.255654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f931240c-7fa7-47c1-b2e3-f918d37cba2b · outbound

This paper cites Financial time series prediction via neural ordinary differential equations approach.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Financial time series prediction via neural ordinary differential equations approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.261536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 525b5d27-0aff-45ec-8591-b165056d947e · outbound

This paper cites Neural odes for data-driven automatic self-design of finite-time output feedback control for unknown nonlinear dynamics.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Neural odes for data-driven automatic self-design of finite-time output feedback control for unknown nonlinear dynamics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.247364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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

This paper cites Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations.

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

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:10:54.403504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 84b02323-8819-4f3a-aa44-0c24733aa513 · outbound

This paper cites Gradient-enhanced kriging for high-dimensional problems.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Gradient-enhanced kriging for high-dimensional problems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.251517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 774eacf5-b012-44ee-88ba-b2237ad11c09 · outbound

This paper cites Learning to solve the ac- opf using sensitivity-informed deep neural networks.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Learning to solve the ac- opf using sensitivity-informed deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.249410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3cca7d68-8157-4ad9-8d56-2fc9566ac5f6 · outbound

This paper cites Learning to optimize power distribution grids using sensitivity- informed deep neural networks.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Learning to optimize power distribution grids using sensitivity- informed deep neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.257696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ca47fab-0e36-4e6e-9f46-64b868a67f1a · outbound

This paper cites Sensitivity, approximation, and uncer- tainty in power system dynamic simulation.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Sensitivity, approximation, and uncer- tainty in power system dynamic simulation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.241535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ad4752e-29ab-4d13-850c-d58757146906 · outbound

This paper cites An annotated timeline of sensitivity analysis.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling An annotated timeline of sensitivity analysis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.243530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 76240b0e-1c7a-4a5e-8463-466e8db71276 · outbound

This paper cites Continuous-in-Depth Neural Networks.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Continuous-in-Depth Neural Networks

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T05:10:54.395932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49256f5e-b07f-430f-809f-74cf728170ce · outbound

This paper cites Bridging neural ode and resnet: A formal error bound for safety verification.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Bridging neural ode and resnet: A formal error bound for safety verification

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:10:54.399900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ba194f1-e9f3-41aa-be03-c282b6ec7a4a · outbound

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TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Unresolved cited work

Reference 20

Resolution: malformed identifier; raw_fallback, observed 2026-05-18T05:10:55.245339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 429c17b5-5b71-454f-9413-8a1c0ffd6bc0 · outbound

This paper cites Second-order trajectory sensitivity analysis of hybrid systems.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Second-order trajectory sensitivity analysis of hybrid systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.259512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3aafd678-bf62-45ec-bda7-7e4608c7b85a · outbound

This paper cites Power system applications of trajectory sen- sitivities.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Power system applications of trajectory sen- sitivities

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.237413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a713cbff-30a6-45ce-a113-c80482ca0ad2 · outbound

This paper cites Trajectory sensitivities: Applications in power systems and estimation accuracy refinement.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Trajectory sensitivities: Applications in power systems and estimation accuracy refinement

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.239564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b025ce55-800b-4630-b0a3-90e3b3ac6ebc · outbound

This paper cites A new approach to dynamic security assessment using trajectory sensitivities.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling A new approach to dynamic security assessment using trajectory sensitivities

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.234868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff718c58-78a1-4026-b6ca-3d98c4be2027 · outbound

This paper cites Trajectory sensitivity analysis of hybrid systems.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Trajectory sensitivity analysis of hybrid systems

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.233000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ddc05f22-ce45-41b6-a636-e8a7316d6347 · outbound

This paper cites Model user guide for generic renewable energy systems.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Model user guide for generic renewable energy systems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.229083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6aec5ec3-62f5-4a56-bd50-c01f3af070ad · outbound

This paper cites Generator model validation and cal- ibration using synchrophasor data.

TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling Generator model validation and cal- ibration using synchrophasor data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T05:10:55.231069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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