Pith. sign in

Paper Citation Record · LEDGER

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 2 inbound Pith citation observations for arXiv:2509.07283.

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

pith.paper-citation-record.v1
2509.07283 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:32:10.836999Z

measured 41 of 41 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:51:31.816695Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T01:58:51.625486Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy25
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c5bd255-62a6-49cf-b4a4-bb972ded7edc · outbound

This paper cites Com- parative study of equivalent circuit models performance in four common lithium-ion batteries: Lfp, nmc, lmo, nca.Batteries, 7(3):51, 2021.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Com- parative study of equivalent circuit models performance in four common lithium-ion batteries: Lfp, nmc, lmo, nca.Batteries, 7(3):51, 2021

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.496832Z

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.

source=pdf_text observed=2026-08-04T22:32:10.448381Z digest=sha256:621e471359f6c393d4414e21c4f19f8b11542a610464522ac09f17bcecd5679e

Observation cb972afe-d424-437b-ae91-f8606598d5ff · outbound

This paper cites A layered swarm optimization method for fitting battery thermal runaway models to accelerating rate calorimetry data.Journal of The Electrochemical Society, 2024.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems A layered swarm optimization method for fitting battery thermal runaway models to accelerating rate calorimetry data.Journal of The Electrochemical Society, 2024

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.444790Z

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.

source=pdf_text observed=2026-08-04T22:32:10.459875Z digest=sha256:77c375c92eae0e8251533857ac515bfcb2f0e5f551da5b7a29cea38c0f3380df

Observation 14e1fd50-7b47-4c6f-a480-0073b45fc620 · outbound

This paper cites Kinetic modeling of gasoline surrogate components and mixtures under engine conditions.Proceedings of the Com- bustion Institute, 33(1):193–200, 2011.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Kinetic modeling of gasoline surrogate components and mixtures under engine conditions.Proceedings of the Com- bustion Institute, 33(1):193–200, 2011

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.402006Z

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.

source=pdf_text observed=2026-08-04T22:32:10.477636Z digest=sha256:086fb3259912387b776ad4f52cc8420a081b9e5d89a74336f0943e97c50e3a17

Observation 67c932d2-c933-4a25-aaac-cde489d55800 · outbound

This paper cites Gauss–seidel iteration for stiff odes from chemical kinetics.SIAM Journal on Scientific Computing, 15(5):1243–1250, 1994.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Gauss–seidel iteration for stiff odes from chemical kinetics.SIAM Journal on Scientific Computing, 15(5):1243–1250, 1994

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.356540Z

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.

source=pdf_text observed=2026-08-04T22:32:10.488602Z digest=sha256:c3528ae360f041fd8e46273bce99761bd9e627c339949500c73be6103604fa0e

Observation 1e04ca2b-db67-4772-9043-5c2fb84ad6c2 · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-04T22:32:12.302777Z

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.

source=pdf_text observed=2026-08-04T22:32:10.502907Z digest=sha256:5c51e73b4c3b185cf096852ee1d0e2d2df3926230a3efda596bea5d93dd75cb5

Observation 4fb54372-4012-475e-9ac1-e42c253cbf62 · outbound

This paper cites Fem-simulation of laminar flame propagation.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Fem-simulation of laminar flame propagation

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.262810Z

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.

source=pdf_text observed=2026-08-04T22:32:10.514240Z digest=sha256:75c59b16805dd9dbcf88f77e2f9d9d29d64fe1b75b1e746e8d0fabb0398a7a1b

Observation 54a00f8d-1c2d-4dc5-9c8d-26e98409bd88 · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-04T22:32:12.217235Z

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.

source=pdf_text observed=2026-08-04T22:32:10.526529Z digest=sha256:c25befc0487781c6a7f75ae60f38ccbbe76cfa48e36689638409308a5bea0873

Observation edb3a221-4552-42b8-b1ce-77a80703e65d · outbound

This paper cites Thermal kinetics comparison of delithiated li [nixcoymn1-xy] o2 cathodes.Journal of Power Sources, 514:230582, 2021.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Thermal kinetics comparison of delithiated li [nixcoymn1-xy] o2 cathodes.Journal of Power Sources, 514:230582, 2021

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.181151Z

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.

source=pdf_text observed=2026-08-04T22:32:10.538189Z digest=sha256:ae2301f48f92f55aa35d4f29cc859ffa06aceccc7de8fe857f443b78a844a8e5

Observation b19d4769-b735-4ab5-894d-75523ebfe831 · outbound

This paper cites Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components.Applied energy, 228:633–644, 2018.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components.Applied energy, 228:633–644, 2018

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.137759Z

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.

source=pdf_text observed=2026-08-04T22:32:10.545693Z digest=sha256:9499e48fe85269916ed9c3d81bb256138fe5a06fb98ff35418af18af4593649f

Observation 61b3a74b-a6b3-4594-9f16-0ae36eb70bff · outbound

This paper cites Chemical reaction neural networks for fitting accelerating rate calorimetry data.Journal of Power Sources, 628:235834, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Chemical reaction neural networks for fitting accelerating rate calorimetry data.Journal of Power Sources, 628:235834, 2025

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.095133Z

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.

source=pdf_text observed=2026-08-04T22:32:10.557193Z digest=sha256:0ba3e0d0678b730b6914112635b71e225c0f52864816507ff4bab2eb570d48e6

Observation 88fc175b-dac1-47b6-8c34-9ae2f96d4ee6 · outbound

This paper cites Identification of parameters for equivalent circuit model of li-ion battery cell with population based optimization algorithms.Ain Shams Engineering Journal, 15(3):102481, 2024.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Identification of parameters for equivalent circuit model of li-ion battery cell with population based optimization algorithms.Ain Shams Engineering Journal, 15(3):102481, 2024

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.065164Z

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.

source=pdf_text observed=2026-08-04T22:32:10.571348Z digest=sha256:f9cd4990f6a5a9909a17b6716acdee3bdffab056c4aa1b6ce94c9825fb825b34

Observation 0f742e1d-2766-4a96-8968-3f4f819312fc · outbound

This paper cites Electrochemical model parameter iden- tification of a lithium-ion battery using particle swarm optimization method.Journal of Power Sources, 307:86–97, 2016.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Electrochemical model parameter iden- tification of a lithium-ion battery using particle swarm optimization method.Journal of Power Sources, 307:86–97, 2016

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:12.025043Z

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.

source=pdf_text observed=2026-08-04T22:32:10.580332Z digest=sha256:f4d9ef25b666ad51407540fb0bac5a51894b52b2de4276a37fec06f0f5774467

Observation c5fc0e62-8b3e-4070-afca-3eeb125b94b7 · outbound

This paper cites Hysteretic tuned mass damper with bumpers for seismic protection: Modeling, identification, and shaking table tests.Journal of Sound and Vibration, 597:118816, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Hysteretic tuned mass damper with bumpers for seismic protection: Modeling, identification, and shaking table tests.Journal of Sound and Vibration, 597:118816, 2025

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.975027Z

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.

source=pdf_text observed=2026-08-04T22:32:10.591796Z digest=sha256:9768a322d5079e064ae8c26d4c5dc129797a186e3e3178ff6aa2daf89277c908

Observation 82061af9-ddf7-41d3-8c2f-3591bb217797 · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-04T22:32:11.942746Z

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.

source=pdf_text observed=2026-08-04T22:32:10.639706Z digest=sha256:e93c0214100fe3c421aee20bca0dc653139f2f0213f6a2d6113d9e821747fdbf

Observation e2776587-9096-4805-965a-0b520702c025 · outbound

This paper cites A physics-informed neural network approach to parameter estimation of lithium-ion battery electro- chemical model.Journal of Power Sources, 621:235271, 2024.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems A physics-informed neural network approach to parameter estimation of lithium-ion battery electro- chemical model.Journal of Power Sources, 621:235271, 2024

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.908554Z

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.

source=pdf_text observed=2026-08-04T22:32:10.652532Z digest=sha256:c58348916ce2429cec67a82f78ae8da6ec55b6a2e1e1a8834e947dd9ecfda651

Observation 5190c88a-d860-4482-a4f6-56d785e27b2b · outbound

This paper cites A physics-informed neural networks framework for model parameter identification of beam-like structures.Mechanical Systems and Signal Processing, 224:112189, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems A physics-informed neural networks framework for model parameter identification of beam-like structures.Mechanical Systems and Signal Processing, 224:112189, 2025

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.855781Z

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.

source=pdf_text observed=2026-08-04T22:32:10.662050Z digest=sha256:156c951b9e5c7be7a72ef15e6834519908ce8970977eeb89c65297394411d037

Observation e0020e3b-df2a-4376-902e-5073fe50e194 · outbound

This paper cites The findability of microkinetic parameters by heterogeneous chemical reaction neural networks (hcrnns).Chemical Engineering Journal, 510:161460, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems The findability of microkinetic parameters by heterogeneous chemical reaction neural networks (hcrnns).Chemical Engineering Journal, 510:161460, 2025

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.807776Z

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.

source=pdf_text observed=2026-08-04T22:32:10.669161Z digest=sha256:e57e0f3b71edbecbc04562f96aa6b727f4933a768cef84ac046fb0e310074827

Observation 2fa4ea45-1d4b-46b8-9425-dd49bc10896d · outbound

This paper cites Accommodating physical reaction schemes in dsc cathode thermal stability analysis using chemical reaction neural networks.Journal of Power Sources, 581:233443, 2023.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Accommodating physical reaction schemes in dsc cathode thermal stability analysis using chemical reaction neural networks.Journal of Power Sources, 581:233443, 2023

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.769736Z

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.

source=pdf_text observed=2026-08-04T22:32:10.675744Z digest=sha256:342ebb646aa1aacf27bfaedea4aa1a445a84fec0b223e64ed0fdbc9681d5238a

Observation cf6aed1d-b535-4fa3-944f-854448f79d7b · outbound

This paper cites On Neural Differential Equations.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems On Neural Differential Equations

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.681899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.681899Z digest=sha256:d4d694307e7e8b52d42b875c5db0857b737d2446bb1a1baec5db87d3fab337f9

Observation e76bd9b2-e525-4f25-8fad-97b117a6ca5d · outbound

This paper cites Chatgpt for programming numerical methods.Journal of Machine Learning for Modeling and Computing, 4(2), 2023.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Chatgpt for programming numerical methods.Journal of Machine Learning for Modeling and Computing, 4(2), 2023

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.718480Z

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.

source=pdf_text observed=2026-08-04T22:32:10.689676Z digest=sha256:dc854b88310795b5d6fdbc4a7c9991bd882ac4165cd4e7892e30dc2848f7df02

Observation e563119b-64f4-46e7-9885-115b2d2d3ec7 · outbound

This paper cites Fine-tuning a large language model for automating com- putational fluid dynamics simulations.Theoretical and Applied Mechanics Letters, page 100594, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Fine-tuning a large language model for automating com- putational fluid dynamics simulations.Theoretical and Applied Mechanics Letters, page 100594, 2025

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.680800Z

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.

source=pdf_text observed=2026-08-04T22:32:10.695396Z digest=sha256:5efbf3f0ed0c05ae3928b0e53d007387c1871ea6168e1ac7d37ac7e7aa89f3cd

Observation dda0080c-840c-463d-ae2a-2a5161eefb9d · outbound

This paper cites Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Openfoamgpt: A retrieval-augmented large language model (llm) agent for openfoam-based computational fluid dynamics.Physics of Fluids, 37(3), 2025

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.701094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.701094Z digest=sha256:0243786256630f8bba50bea319f01d6d0c77f0ced9905abe943a27d27b32fabb

Observation 208ddf37-ce73-4e77-a1b9-59ee9f923015 · outbound

This paper cites Ai agents in engineering design: a multi-agent framework for aesthetic and aerodynamic car design.arXiv preprint arXiv:2503.23315, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Ai agents in engineering design: a multi-agent framework for aesthetic and aerodynamic car design.arXiv preprint arXiv:2503.23315, 2025

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.706620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.706620Z digest=sha256:e14006bd8ff3fde55b313354ccebfd4939d62c9ccdb4a4e61460b712815325b4

Observation 7e199c4a-760c-4209-a21d-9b44f23d66ea · outbound

This paper cites From concept to manufacturing: Evaluating vision-language models for engineering design.Artificial Intelligence Review, 58(9):288, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems From concept to manufacturing: Evaluating vision-language models for engineering design.Artificial Intelligence Review, 58(9):288, 2025

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.712255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.712255Z digest=sha256:0cf1c872736c005a654e1d15298b17384cd1fa1acc35f4a116d531938902c99c

Observation 10f03d34-84c6-4b06-827a-dcea45acabf4 · outbound

This paper cites How an ai-enabled software product development life cycle will fuel innovation.https://www.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems How an ai-enabled software product development life cycle will fuel innovation.https://www

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.589553Z

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.

source=pdf_text observed=2026-08-04T22:32:10.717930Z digest=sha256:7c76e09a7b7b6cca4ee67c92f1370890b17a1546f5fac2c5c128c9401d9e54d9

Observation 4b25d573-2c0d-4157-b412-1f3cbaab4517 · outbound

This paper cites Robertson’s example for stiff differential equations.Arizona State Univer- sity, Technical report, 1996.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Robertson’s example for stiff differential equations.Arizona State Univer- sity, Technical report, 1996

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.566384Z

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.

source=pdf_text observed=2026-08-04T22:32:10.725416Z digest=sha256:9a2c60b25bfdf3f9a8d25563d80cebcd30428b40e263d5783933d6be86ad721e

Observation 58322259-493c-4b11-971e-eeff0d4c3dea · outbound

This paper cites Stiff-pinn: Physics-informed neural network for stiff chemical kinetics.The Journal of Physical Chemistry A, 125(36):8098–8106, 2021.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Stiff-pinn: Physics-informed neural network for stiff chemical kinetics.The Journal of Physical Chemistry A, 125(36):8098–8106, 2021

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.731272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.731272Z digest=sha256:36c75219f39b396c1ffa13d5c895f8b936e9bd62e67b61e3abe9e50e927d840d

Observation f3557457-e065-4249-81c7-04846b5f9a90 · outbound

This paper cites Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-04T22:32:10.921856Z

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.

source=pdf_text observed=2026-08-04T22:32:10.737809Z digest=sha256:7499a79975bd628cc008d2407615b6f5d83e339c379e088bf9ad821bd9a2d659

Observation 214365d9-2d37-41ec-956f-de7fa41c0282 · outbound

This paper cites Analysis of an experimental technique for determining van der pol parameters of a transistor oscillator.IEEE transactions on microwave theory and techniques, 46(7):914–922, 1998.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Analysis of an experimental technique for determining van der pol parameters of a transistor oscillator.IEEE transactions on microwave theory and techniques, 46(7):914–922, 1998

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.470286Z

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.

source=pdf_text observed=2026-08-04T22:32:10.744606Z digest=sha256:e36476b894253fd77d9c1b7c1f80ba291ce14836e437b4305a2c8e2138b92b56

Observation 8b039267-94a0-4794-80da-9a291dc6e7f5 · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-04T22:32:11.427885Z

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.

source=pdf_text observed=2026-08-04T22:32:10.751880Z digest=sha256:68587e345cfcc20d861883b860a2d05af74e92e6395305d4f73b3377e490ffaf

Observation e460f28e-69fb-4688-ac3b-0d11ae205a28 · outbound

This paper cites Thermal runaway characterization of cylindrical lithium-ion and sodium-ion batteries with various sizes and energy contents.Journal of Power Sources, 648:237240, 2025.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Thermal runaway characterization of cylindrical lithium-ion and sodium-ion batteries with various sizes and energy contents.Journal of Power Sources, 648:237240, 2025

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.382325Z

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.

source=pdf_text observed=2026-08-04T22:32:10.764857Z digest=sha256:f3a375beff1fc5fd4911c192812470525a49ab7bea87bca943f555c3ee467d29

Observation edd575c1-f5d7-4de7-bc75-37b614454ab7 · outbound

This paper cites Experimental and simulation-based characterization of thermal runaway in lithium-ion batteries using altair simlab®.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Experimental and simulation-based characterization of thermal runaway in lithium-ion batteries using altair simlab®

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.339607Z

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.

source=pdf_text observed=2026-08-04T22:32:10.771596Z digest=sha256:893144ebeb528a431e79e0fad1d1fdeadb494c9facd36e802d887c9aab685e9c

Observation 9036ba84-f1a4-477e-b7a2-7570d0f33fa0 · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-04T22:32:11.275976Z

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.

source=pdf_text observed=2026-08-04T22:32:10.777418Z digest=sha256:68a90ba9f599afe23bdd08f894a442829bd52b96f93bd63bd36658d9e02db568

Observation 9e0d5e36-21a7-4a7c-b630-6cbe80452c5d · outbound

This paper cites an unresolved cited work.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Unresolved cited work

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.783414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.783414Z digest=sha256:ac5d52960f9462978b11551deac6a79f5eb7639e2a199f80220b7d2d98fa8465

Observation 06ab4ba5-f59a-431f-bba7-d7cbf5134afe · outbound

This paper cites Φ flow (PhiFlow): Differentiable simulations for pytorch, tensorflow and jax.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Φ flow (PhiFlow): Differentiable simulations for pytorch, tensorflow and jax

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.202459Z

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.

source=pdf_text observed=2026-08-04T22:32:10.793376Z digest=sha256:b84c438864812e1fdf31091fe5dd63e588314d66579d5ffe40d90cb72c61333c

Observation 366dc150-c1f5-4d6a-a5d6-27d4e8c04015 · outbound

This paper cites Particle swarm optimization.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Particle swarm optimization

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.805376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.805376Z digest=sha256:34db1123edcdde348f08ee8c1478ed65e2cc9da337ba04650eb28228e8b70fc8

Observation 54d1aa45-0ed8-402c-a400-98337019754f · outbound

This paper cites Neural ordinary differential equations.Advances in neural information processing systems, 31, 2018.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Neural ordinary differential equations.Advances in neural information processing systems, 31, 2018

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:10.813015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:10.813015Z digest=sha256:ba72a475aaa8056a775a84f414b07bd004fe6172da15fe97c81da497b00a2b95

Observation fa782936-47b3-44db-84b4-4d84d37d5cf8 · outbound

This paper cites Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks.Combustion and Flame, 240:111992, 2022.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Autonomous kinetic modeling of biomass pyrolysis using chemical reaction neural networks.Combustion and Flame, 240:111992, 2022

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.090364Z

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.

source=pdf_text observed=2026-08-04T22:32:10.830753Z digest=sha256:794fe9268c07d039b83e357146ce576a92f9950b4ced33df388434b47f0752a6

Observation 4d6026d1-77f2-457f-a7f7-a3193cecfa5d · outbound

This paper cites Jax md: a framework for differentiable physics.

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems Jax md: a framework for differentiable physics

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:32:11.048498Z

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.

source=pdf_text observed=2026-08-04T22:32:10.836999Z digest=sha256:c45968ad3a0843949100579aaf82346a9574060e6522189bf00ea77fcf9240f7

Pith citing papers

Observation 57f763e4-3ac0-4882-a4bb-0e6cd1ab02c1 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-17T01:58:51.628150Z

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.

source=pdf_text observed=2026-05-17T01:55:52.535545Z digest=sha256:fd9385c766acb8080fb15e179ad98c553e0dab21387462bd971d7fbe70416779

Observation db7f6a86-c4de-4e9f-8c2d-082b4b5c635f · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-03T18:51:31.816695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:51:31.816695Z digest=sha256:4810e4a9b4554059a74d47df2b6780181623a6a3c141aa4e7a4e7c4d1b034ce4