Paper Citation Record · LEDGER

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.22811.

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

pith.paper-citation-record.v1
2607.22811 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:30:00.118801Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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unresolved33
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External citation measurements

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Outbound references

Observation 120b5d83-358a-49c1-b076-bea7d26406d3 · outbound

This paper cites Hybrid semi-parametric mathematical systems: Bridging the gap between systems biology and process engineering.Journal of biotechnology, 132(4):418–425, 2007.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Hybrid semi-parametric mathematical systems: Bridging the gap between systems biology and process engineering.Journal of biotechnology, 132(4):418–425, 2007

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.071483Z

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Observation f86bc29c-d14c-4743-8a25-5e0ace08e27a · outbound

This paper cites Grey-box modelling and identification using physical knowledge and bayesian techniques.Automatica, 29(2):285–308, 1993.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Grey-box modelling and identification using physical knowledge and bayesian techniques.Automatica, 29(2):285–308, 1993

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.197609Z

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Observation bcc1cd32-418d-4b03-be80-ea6acdffbabd · outbound

This paper cites A hybrid neural network-first principles approach to process modeling.AIChE Journal, 38(10):1499–1511, 1992.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How A hybrid neural network-first principles approach to process modeling.AIChE Journal, 38(10):1499–1511, 1992

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.319544Z

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Observation e1ba7a41-2085-4295-a951-465ed34adf4e · outbound

This paper cites Modeling chemical processes using prior knowledge and neural networks.AIChE Journal, 40(8):1328–1340, 1994.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Modeling chemical processes using prior knowledge and neural networks.AIChE Journal, 40(8):1328–1340, 1994

Reference 4

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Observation f3d89d99-981c-4b6d-aadd-1a14dbe686e4 · outbound

This paper cites Strategy for dynamic process modeling based on neural networks in macroscopic balances.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Strategy for dynamic process modeling based on neural networks in macroscopic balances

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.634153Z

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Observation 0958caeb-c27c-4855-8017-1689a9e5973c · outbound

This paper cites Combining neural and conventional paradigms for modelling, prediction and control.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Combining neural and conventional paradigms for modelling, prediction and control

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.736056Z

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Observation f57a3626-96c0-44d0-9ded-83e707b78c1d · outbound

This paper cites Extrapolability of structured hybrid models: a key to optimization of complex processes.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Extrapolability of structured hybrid models: a key to optimization of complex processes

Reference 7

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Observation 624b6782-d6b8-422b-a673-8e0db636ae0b · outbound

This paper cites Elsevier, 2001.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Elsevier, 2001

Reference 8

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Observation 7013cdd9-bbf5-409f-a9a4-934a7279a5cf · outbound

This paper cites Local identification of scalar hybrid models with tree structure.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Local identification of scalar hybrid models with tree structure

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:56.968227Z

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Observation ce9ad160-5e92-45dd-8032-610697c00d6e · outbound

This paper cites Understanding and applying the extrapolation properties of serial gray-box models.AIChE journal, 44(5):1071–1089, 1998.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Understanding and applying the extrapolation properties of serial gray-box models.AIChE journal, 44(5):1071–1089, 1998

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.055439Z

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Observation 4492dbb7-04d6-43be-90f5-3008972fc1a4 · outbound

This paper cites Hybrid semi-parametric modeling in process systems engineering: Past, present and future.Computers & Chemical Engineering, 60:86–101, 2014.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Hybrid semi-parametric modeling in process systems engineering: Past, present and future.Computers & Chemical Engineering, 60:86–101, 2014

Reference 11

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Observation 571db92f-610b-4c7e-9077-e3f8b620792d · outbound

This paper cites Hybrid neural network modeling of a full-scale industrial wastewater treatment process.Biotechnology and bioengineering, 78(6):670–682, 2002.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Hybrid neural network modeling of a full-scale industrial wastewater treatment process.Biotechnology and bioengineering, 78(6):670–682, 2002

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.266179Z

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Observation d55a36b0-8aed-4e01-8751-0679a70e3f49 · outbound

This paper cites First-principles, data-based, and hybrid modeling and optimiza- tion of an industrial hydrocracking unit.Industrial & engineering chemistry research, 45(23):7807–7816, 2006.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How First-principles, data-based, and hybrid modeling and optimiza- tion of an industrial hydrocracking unit.Industrial & engineering chemistry research, 45(23):7807–7816, 2006

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.417926Z

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Observation 67380d2c-3513-479c-9195-8db528187ed7 · outbound

This paper cites Knowledge based modular networks for process modelling and control.Computers & Chemical Engineering, 25(4-6):783–791, 2001.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Knowledge based modular networks for process modelling and control.Computers & Chemical Engineering, 25(4-6):783–791, 2001

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.587595Z

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Observation 5aabc434-54ea-4621-a730-e3997c89a46f · outbound

This paper cites Bioprocess hybrid parametric/nonparametric modelling based on the concept of mixture of experts.Biochemical Engineering Journal, 39(1):190–206, 2008.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Bioprocess hybrid parametric/nonparametric modelling based on the concept of mixture of experts.Biochemical Engineering Journal, 39(1):190–206, 2008

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.739893Z

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Observation 1c9e8094-5fe3-481f-a3a3-8f46395f296c · outbound

This paper cites Fuzzy identification of systems and its applications to modeling and control.IEEE transactions on systems, man, and cybernetics, (1):116–132, 1985.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Fuzzy identification of systems and its applications to modeling and control.IEEE transactions on systems, man, and cybernetics, (1):116–132, 1985

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:57.906000Z

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Observation 22a08fd1-1292-49b0-82ee-8248f21d0049 · outbound

This paper cites A structured modeling approach for dynamic hybrid fuzzy-first principles models.Journal of Process Control, 12(5):605–615, 2002.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How A structured modeling approach for dynamic hybrid fuzzy-first principles models.Journal of Process Control, 12(5):605–615, 2002

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.068876Z

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Observation f65fb8c0-cd0c-454f-8f40-2ea0c4d35c1c · outbound

This paper cites The validity domain of hybrid models and its application in process optimization.Chemical Engineering and Processing: Process Intensification, 46(11):1054–1066, 2007.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How The validity domain of hybrid models and its application in process optimization.Chemical Engineering and Processing: Process Intensification, 46(11):1054–1066, 2007

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.182038Z

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Observation 9483c20b-853d-460a-89af-d651e3648214 · outbound

This paper cites Efficient reengineering of meso-scale topologies for functional networks in biomed- ical applications.Journal of Mathematics in Industry, 1(1):6, 2011.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Efficient reengineering of meso-scale topologies for functional networks in biomed- ical applications.Journal of Mathematics in Industry, 1(1):6, 2011

Reference 19

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Observation 88bef452-74f5-4a6b-9279-cd4941cb116a · outbound

This paper cites A hybrid modeling framework for gen- eralizable and interpretable predictions of icu mortality across multiple hospitals.Scientific reports, 14(1):5725, 2024.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How A hybrid modeling framework for gen- eralizable and interpretable predictions of icu mortality across multiple hospitals.Scientific reports, 14(1):5725, 2024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.423749Z

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Observation ecbf4aa3-4836-44e9-945f-7e5883b57b3f · outbound

This paper cites CRC Press, 2018.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How CRC Press, 2018

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.510912Z

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Observation b82dca4f-8ba0-4a83-ae01-4ae2c733bd29 · outbound

This paper cites Dimensions of Neural-symbolic Integration - A Structured Survey.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Dimensions of Neural-symbolic Integration - A Structured Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.613838Z

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Observation ca326683-869d-43a4-bbca-b02437bf2900 · outbound

This paper cites Neural-symbolic learning and reasoning: A survey and interpre- tation.Frontiers in artificial intelligence and applications, 342:1–51, 2022.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Neural-symbolic learning and reasoning: A survey and interpre- tation.Frontiers in artificial intelligence and applications, 342:1–51, 2022

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.738187Z

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Observation c074e9e1-d722-429e-8ae5-b011e831d525 · outbound

This paper cites From statistical relational to neurosymbolic artificial intelligence: A survey.Artificial Intelligence, 328:104062, 2024.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How From statistical relational to neurosymbolic artificial intelligence: A survey.Artificial Intelligence, 328:104062, 2024

Reference 24

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Observation 2920ee77-78d0-45b2-bae9-a0e598e8607d · outbound

This paper cites Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:58.936145Z

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Observation 55d0a703-7ef2-4ec1-8c99-379011d13d2e · outbound

This paper cites Neurosymbolic ai: The 3 rd wave.Artificial Intelligence Review, 56(11):12387–12406, 2023.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Neurosymbolic ai: The 3 rd wave.Artificial Intelligence Review, 56(11):12387–12406, 2023

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:59.124540Z

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Observation 0e4fbf4f-d225-46e7-b2f8-97c9978eaaca · outbound

This paper cites Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:59.321684Z

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Observation 48027ae6-d22a-4864-84ad-4c810eb37ff8 · outbound

This paper cites Probabilistic inference in hybrid domains by weighted model integration.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Probabilistic inference in hybrid domains by weighted model integration

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:59.477188Z

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Observation 2ae76c8d-bf62-4d73-bf1d-767899d2e5d5 · outbound

This paper cites Not all neuro-symbolic con- cepts are created equal: Analysis and mitigation of reasoning shortcuts.Advances in Neural Information Processing Systems, 36:72507–72539, 2023.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Not all neuro-symbolic con- cepts are created equal: Analysis and mitigation of reasoning shortcuts.Advances in Neural Information Processing Systems, 36:72507–72539, 2023

Reference 29

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Observation 71f4da92-b4df-4962-ae40-0e3a57615d84 · outbound

This paper cites Defining neurosymbolic AI.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Defining neurosymbolic AI

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T04:29:59.717592Z

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Observation 6fdee1bf-2b4e-4649-9203-d71c980a1b9d · outbound

This paper cites Combining prior knowledge with data driven mod- eling of a batch distillation column including start-up.Computers & chemical engineering, 27(7):1021– 1030, 2003.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How Combining prior knowledge with data driven mod- eling of a batch distillation column including start-up.Computers & chemical engineering, 27(7):1021– 1030, 2003

Reference 31

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Observation 116d0e73-e6e3-472b-9941-0e59122def5b · outbound

This paper cites A training strategy for hybrid models to break the curse of dimensionality.Plos one, 17(9):e0274569, 2022.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How A training strategy for hybrid models to break the curse of dimensionality.Plos one, 17(9):e0274569, 2022

Reference 32

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Observation 2f2f889c-7b75-4846-9a72-781c285d97dd · outbound

This paper cites unseen frac.

From Hybrid Mechanistic--Data-Driven Modeling Toward Neuro-Symbolic AI: What, Why, and How unseen frac

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T04:30:00.118801Z

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Pith citing papers

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