Pith. sign in

Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.448381Z digest=sha256:b4982c1fdef072f9e4796ed58d00165dc00d3ec17fbc903666369bbb2d780de1

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.459875Z digest=sha256:45ce0ef049e5ca650673ae014ef8a0b905271a96caf33b23520dc1a7d145ddb2

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.477636Z digest=sha256:9c0021a69bb0a9f13db010a0092b8c5563d78698ec65500067c98806466b0caf

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.488602Z digest=sha256:b329ea62069d0580dadca6fdacd9284848154fd5ef045234d5ee4233df73fbc7

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.502907Z digest=sha256:3a0a53e72b6f156bd688ba5a2db9382488216539e42ec1a907cfb34775384b97

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.514240Z digest=sha256:9a8adf9b039d6d78eb8c20f5363c97c23e3deb5663d803dce0c6dc90527622ac

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.526529Z digest=sha256:f368ded1b13041556b63727d0e95e8a2c95daa5f9960179dcaa3db45848175d0

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.538189Z digest=sha256:2557582716ba64218057e28593f7229831a950f74f0267809363cc73c72677f1

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.545693Z digest=sha256:3a3f273b9463e49e3c96e60ddb0f82f9ff0504fe8cc593813824b653dd11a8ee

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.557193Z digest=sha256:625e39150969ac07df9fe9a653acef7a1e7da152405c0c71286ff0f5514b5c3d

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.571348Z digest=sha256:d2a309b56bc4ad82ab869de22a085c7a74f9f21d16cc7450e349d3e325934c5c

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.580332Z digest=sha256:fc197a65e4ac0e53ce6fc15245833b2f0029d3b20eda8eb958a20d8725e39f39

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.591796Z digest=sha256:52dece9986da06f4d8fa3155d6f66decdecc0f758914310624a3ff4c0e322cd3

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.639706Z digest=sha256:4f5f80c2c7729e722e346ee1d4233c9b8858f6d15261aa01cd8109466c20cc58

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.652532Z digest=sha256:b2c2e86d4ad88366e3f1dbd1dd5221301dee4c2e56aa48beedb19dc3650e9dcb

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.662050Z digest=sha256:b030ec93d5dd62e767e1583189fa1b28de37a50fc83ce23e0072ebc8b5607623

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.669161Z digest=sha256:54ef9dc65fea48a4a47c9df9fbceb21a9fac573c00cd935303bfb33eaf45daa3

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.675744Z digest=sha256:4a4db6d344023ebb92366fbbace5b59935cadf719487d18d3a79d3d7349d5cf3

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=sha256:72289099a52031f10c4f3c8662d940bf773fa7600b96699c202a6b6363497157

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.689676Z digest=sha256:dca8d88dd78c423077f531659f5416e930ce814bf8cc8ee90696b619c7973d87

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.695396Z digest=sha256:1a143089a86396b74ff2ff06c846fdf5d1b0f3b17d23de28c2938b23382b0b2c

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=sha256:2d3b69735eee45bbfaed03c6a99ee34ece9ad1a9a7c6583d376a4f425cfc33d7

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=sha256:7251934d9e303a488f03c0576da8a98a8f9e9490a3e06a70f2129c4907775eb6

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=sha256:0ec9dc0a9deaf8e6206b884ebbe36a3700145df583fb12bee8b0f36b6094f8bf

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.717930Z digest=sha256:626fdcb23610d7e50cb4e09ebcb3edea3ec68acce128f7e855e58c257af023f8

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.725416Z digest=sha256:fa86c7894af9e56c3fdb862e6327c434d942621263f611fe1cbb4813f5831e09

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=sha256:194a05f85d9755db4bd6c3470747b4cfe1f0853f9d5b399c1ce980e5807e6ce9

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.737809Z digest=sha256:17636615b89dc90cf94b1940e44a006567076a2ce0ea401305e831bf2dc261f8

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.744606Z digest=sha256:15c8e01c8aadc3e79ee94b40c9d799615d06b8f1130248ee06abd5c4779d71fa

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.751880Z digest=sha256:ccbda7156b46441bb7f211e3b6098517ef879bd633fb0ba93463da41508853d6

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.764857Z digest=sha256:c0015239f8b32d06b7926fd7d2fb99dfafff1ace778135aa2e91c6ab6166a351

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.771596Z digest=sha256:b46691abe3906144d118f3d1d5b1127415a847f206382448813c77c10272ca54

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.777418Z digest=sha256:165939a91839c3d94e332574370a386f201cc7d8bc44a107ca9430fb3e858328

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=sha256:7f682eb6bc6811c8dbba9239bd8ef75f84972aef8a38526e9a67bded0a415d14

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.793376Z digest=sha256:2b6aa26d42ab7bfc610e0eb3e66d2d6363b326cff9ff935b7bef49288f8d6bc8

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:b21e00107b3c1700ee254f49c89a50a0d874dcc3baf064c3016f74e127876f7d

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=sha256:2f6261b5e3dc80e4b0dc472a5fdd72e8e876793b83e18cb0b8f9070ea8d4e2d8

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.830753Z digest=sha256:b2d926383f823f255cb9d766a154cd21b999713baaa8297d1631c19ac1909939

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-04T22:32:10.836999Z digest=sha256:a68a27474d96d7e3254085b8d826c3c9de7fab77a36f6c03adc9a52ea84fb7a9

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-17T01:55:52.535545Z digest=sha256:ef5262d2f3ab3fef9d4913085159ad070115cde8c8c40bb4ada082fb8f081404

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:f67a2963f2341c27084d7a0de5d171c315be59a2c49e29f265dfe23c65892b9c