Paper Citation Record · LEDGER

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.14252.

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

pith.paper-citation-record.v1
2505.14252 v2

Coverage vector

measured 38 of 38 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 62f48f5a-6f61-41ee-a54d-7197c49afcfd · outbound

This paper cites Nature machine intelligence 1(5), 206–215 (2019).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Nature machine intelligence 1(5), 206–215 (2019)

Reference 1

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This paper cites Advances in neural information processing systems 31 (2018).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Advances in neural information processing systems 31 (2018)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:59.939292Z

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Observation bdf9e224-9e5f-48d7-a8f8-15de16051fe4 · outbound

This paper cites Nature Reviews Physics3(6), 422–440 (2021) 35.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Nature Reviews Physics3(6), 422–440 (2021) 35

Reference 3

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Observation 8a924eb3-b391-4092-b04a-265e098d976f · outbound

This paper cites Reliability Engineering & System Safety 217, 107961 (2022).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Reliability Engineering & System Safety 217, 107961 (2022)

Reference 4

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Observation c82db6c7-1c60-4055-961a-3d3ffc16c6b8 · outbound

This paper cites Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Reference 5

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Observation 6dcd8970-71a5-42bf-b1d2-6be73b5d552a · outbound

This paper cites International Journal of Heat and Mass Transfer 217, 124671 (2023).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks International Journal of Heat and Mass Transfer 217, 124671 (2023)

Reference 6

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Observation 9815af9b-2cff-4684-a707-c7cc7706d3c6 · outbound

This paper cites Computers & Fluids 248, 105632 (2022).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Computers & Fluids 248, 105632 (2022)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:58.840782Z

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Observation 425e0009-278e-4006-b5ea-d578885443cb · outbound

This paper cites Algorithms 15(2), 53 (2022).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Algorithms 15(2), 53 (2022)

Reference 8

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Observation 25c71a3c-9a29-458a-9c50-d6bba3d55b03 · outbound

This paper cites PhD thesis, Universit´ e Cˆ ote d’Azur, Inria, CNRS, LJAD (2023).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks PhD thesis, Universit´ e Cˆ ote d’Azur, Inria, CNRS, LJAD (2023)

Reference 9

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Observation 017daeda-0786-4fa7-9e78-27f65a99c2e0 · outbound

This paper cites Journal of Computational Physics451, 110844 (2022).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Journal of Computational Physics451, 110844 (2022)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:58.017884Z

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Observation 0cf0442b-7cb7-4100-b9f0-80ecb235c90f · outbound

This paper cites Journal of Computational physics 378, 686–707 (2019).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Journal of Computational physics 378, 686–707 (2019)

Reference 11

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Observation 8dae650d-6c63-4234-bf8e-ae52a5b9b0cc · outbound

This paper cites Elsevier (2024).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Elsevier (2024)

Reference 12

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Observation 5d76081d-ff76-433e-9b42-ff5644acfd30 · outbound

This paper cites Inverse Problems 41(3), 035006 (2025).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Inverse Problems 41(3), 035006 (2025)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:57.226311Z

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Observation 8fbb337a-ff2b-4a14-912a-56c4620da4a5 · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks DeepXDE: A deep learning library for solving differential equations

Reference 14

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Observation 61a3b7f6-395c-4e57-bb26-818c0fa74929 · outbound

This paper cites Proceedings of the Royal Society A 474(2219), 20180335 (2018) 36.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Proceedings of the Royal Society A 474(2219), 20180335 (2018) 36

Reference 15

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Observation 2be9177d-e036-4487-912b-7889dffe5870 · outbound

This paper cites Proceedings of the national academy of sciences 113(15), 3932–3937 (2016).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Proceedings of the national academy of sciences 113(15), 3932–3937 (2016)

Reference 16

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Observation 9cf9ad50-5de2-4af7-984b-77c8ebec0d27 · outbound

This paper cites Nature communications 12(1), 6136 (2021).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Nature communications 12(1), 6136 (2021)

Reference 17

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Observation 5a7970fd-3cda-46dc-941f-478751d25e59 · outbound

This paper cites Machine Learning 114(1), 1–36 (2025).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Machine Learning 114(1), 1–36 (2025)

Reference 18

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Observation b1851d1a-c781-4ac7-8665-567a0f29ac5a · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Advances in neural information processing systems 30 (2017)

Reference 19

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Observation 02ea2483-3189-48f8-ab6b-8c4c1b24246d · outbound

This paper cites In: International Conference on Machine Learning, pp.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks In: International Conference on Machine Learning, pp

Reference 20

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Observation 6d6dcff1-f932-40f9-87bb-b0b56509c720 · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Advances in neural information processing systems 30 (2017)

Reference 21

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Observation 1da0f846-b379-4289-91c3-742c74ca63cf · outbound

This paper cites Parameters 22(72K), 84.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Parameters 22(72K), 84

Reference 22

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Observation baf199ba-849c-4cbb-9430-0ab3c61b6ba2 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 23

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Observation 2727b16e-b6dd-4267-bcd8-a18fdbfc3abb · outbound

This paper cites Physical Review Research 4(2), 023174 (2022).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Physical Review Research 4(2), 023174 (2022)

Reference 24

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Observation c3d94cf6-a91b-443a-8019-da22c1026cdd · outbound

This paper cites Journal of Computational Physics 399, 108925 (2019).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Journal of Computational Physics 399, 108925 (2019)

Reference 25

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Observation c60e6d0e-d93e-4336-9d87-a11e5d3fe2e1 · outbound

This paper cites PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

Reference 26

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Observation 8601ff3c-3c9b-4a98-8519-0a7a08023d0d · outbound

This paper cites Journal of the Royal Statistical Society Series B: Statistical Methodology 58(1), 267–288 (1996).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Journal of the Royal Statistical Society Series B: Statistical Methodology 58(1), 267–288 (1996)

Reference 27

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Observation 29552b33-16d7-4485-89d8-a262d293b7d7 · outbound

This paper cites IEEE Access 7, 1404–1423 (2018) 37.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks IEEE Access 7, 1404–1423 (2018) 37

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:55.547467Z

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Observation e37f9f77-6c6c-41fe-9f87-429ee16a9a49 · outbound

This paper cites Science advances 3(4), 1602614 (2017).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Science advances 3(4), 1602614 (2017)

Reference 29

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Observation 2a51aa83-7eac-4055-a055-5028617b70bc · outbound

This paper cites Nature methods 17(3), 261–272 (2020).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Nature methods 17(3), 261–272 (2020)

Reference 30

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This paper cites Nature Methods 17, 261–272 (2020) https://doi.org/10.1038/ s41592-019-0686-2.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Nature Methods 17, 261–272 (2020) https://doi.org/10.1038/ s41592-019-0686-2

Reference 31

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Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Unresolved cited work

Reference 32

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Observation 8a48e56b-b643-4a57-942c-b48358cbbecd · outbound

This paper cites Peerj computer science 7, 623 (2021).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Peerj computer science 7, 623 (2021)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.674623Z

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Observation 847517e4-97cc-47d7-b9f0-b9cd2c42f2c1 · outbound

This paper cites Data Mining and Knowledge Discovery 37(2), 788–832 (2023).

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Data Mining and Knowledge Discovery 37(2), 788–832 (2023)

Reference 34

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Observation 1f2ce824-e29d-4755-bc4e-b4ff20eaf9fb · outbound

This paper cites Available at https://jsdokken.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Available at https://jsdokken

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.221548Z

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Observation da7db0c9-d9a2-4eb3-a88a-e987846b4c74 · outbound

This paper cites Available at https://wwwold.mathematik.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Available at https://wwwold.mathematik

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:54.028379Z

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This paper cites Computer Methods in Applied Mechanics and Engineering 396, 115100 (2022) https://doi.org/10.1016/j.cma.2022.115100.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Computer Methods in Applied Mechanics and Engineering 396, 115100 (2022) https://doi.org/10.1016/j.cma.2022.115100

Reference 37

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Observation 0073fb32-6756-44d5-b2e9-a191a0df6796 · outbound

This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 38

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Pith citing papers

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