Paper Citation Record · LEDGER

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

As of 10 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 1 inbound Pith citation observation for arXiv:2607.29225.

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

pith.paper-citation-record.v1
2607.29225 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

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measured 71 of 71 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:52:16.783856Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:52:16.889411Z

Reference resolution

70 of 70 outbound references displayed

verified exact16
verified fuzzy0
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External citation measurements

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Outbound references

Observation 407d9c13-4888-48d0-ba1c-08b8c4009312 · outbound

This paper cites Technical Report January, University of Wisconsin–Madison, Madison (2009).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Technical Report January, University of Wisconsin–Madison, Madison (2009)

Reference 1

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This paper cites Proceedings of the IEEE104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Proceedings of the IEEE104(1), 148–175 (2016) https://doi.org/10.1109/JPROC.2015.2494218

Reference 2

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Observation b2cb0d40-1f64-4bfd-a6da-095696f5b7b7 · outbound

This paper cites Targeted materials discovery using Bayesian algorithm execution.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Targeted materials discovery using Bayesian algorithm execution

Reference 3

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Observation 75121f81-380c-482d-b6a0-4a2587ebbb0e · outbound

This paper cites Digital Discovery3(6), 1086–1100 (2024) https://doi.org/10.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Digital Discovery3(6), 1086–1100 (2024) https://doi.org/10

Reference 4

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This paper cites Composite Structures 351, 118597 (2025) https://doi.org/10.1016/j.compstruct.2024.118597.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Composite Structures 351, 118597 (2025) https://doi.org/10.1016/j.compstruct.2024.118597

Reference 5

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This paper cites Digital Discovery4, 3753–3763 (2025) https://doi.org/10.1039/d5dd00237k.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Digital Discovery4, 3753–3763 (2025) https://doi.org/10.1039/d5dd00237k

Reference 6

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Observation 80ed0262-4ab3-467f-bd91-4c3a16e3c809 · outbound

This paper cites MIT Press, Cambridge, MA (2006).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery MIT Press, Cambridge, MA (2006)

Reference 7

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This paper cites In: 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023 (2023).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery In: 2023 IEEE High Performance Extreme Computing Conference, HPEC 2023 (2023)

Reference 8

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This paper cites Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

Reference 9

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Observation e4c19a1e-23a6-4968-9f81-4b61468366a5 · outbound

This paper cites Computer Physics Communications294, 108955 (2024) https://doi.org/ 10.1016/j.cpc.2023.108955 arXiv:2201.06809.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Computer Physics Communications294, 108955 (2024) https://doi.org/ 10.1016/j.cpc.2023.108955 arXiv:2201.06809

Reference 10

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Observation 51c2b690-08b1-415d-a75a-bf080fc29f20 · outbound

This paper cites Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB

Reference 11

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This paper cites Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)

Reference 12

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Observation 6112f051-4684-4cf2-943e-c05c367d8f2a · outbound

This paper cites Advances in Neural Information Processing Systems37(NeurIPS), 1–25 (2024).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Advances in Neural Information Processing Systems37(NeurIPS), 1–25 (2024)

Reference 13

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Observation e48a76f3-07e2-4bac-a4aa-5626de2aec42 · outbound

This paper cites Memory-Based Dual Gaussian Processes for Sequential Learning.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Memory-Based Dual Gaussian Processes for Sequential Learning

Reference 14

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This paper cites Nature Computational Science 5(January) (2024) https://doi.org/10.1038/s43588-024-00744-y.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nature Computational Science 5(January) (2024) https://doi.org/10.1038/s43588-024-00744-y

Reference 15

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Observation b44e9326-4563-4468-81b2-2e446a2fdfda · outbound

This paper cites Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks

Reference 16

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Observation f1d026dd-a574-4bc7-90fd-20ca9a601bf3 · outbound

This paper cites In: 12th International Conference on Learning Representations, ICLR 2024 (2024).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery In: 12th International Conference on Learning Representations, ICLR 2024 (2024)

Reference 17

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Observation ada4e110-79af-42aa-aceb-ef8dd04b711d · outbound

This paper cites Mondrian Forests for Large-Scale Regression when Uncertainty Matters.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Mondrian Forests for Large-Scale Regression when Uncertainty Matters

Reference 18

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This paper cites Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS

Reference 19

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This paper cites npj Computational Materials7(1), 1–12 (2021) https: //doi.org/10.1038/s41524-021-00662-x.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery npj Computational Materials7(1), 1–12 (2021) https: //doi.org/10.1038/s41524-021-00662-x

Reference 20

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Observation 13c77370-12d6-41ac-be21-ec53ffb412a3 · outbound

This paper cites Scalable Bayesian Optimization Using Deep Neural Networks.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Scalable Bayesian Optimization Using Deep Neural Networks

Reference 21

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This paper cites Challenges6(1), 117–157 (2015) https://doi.org/10.3390/ challe6010117.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Challenges6(1), 117–157 (2015) https://doi.org/10.3390/ challe6010117

Reference 22

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This paper cites Journal of Building Engineering91(May), 109519 (2024) https://doi.org/10.1016/j.jobe.2024.109519.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Building Engineering91(May), 109519 (2024) https://doi.org/10.1016/j.jobe.2024.109519

Reference 23

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This paper cites Computer55(7), 18–28 (2022) https://doi.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Computer55(7), 18–28 (2022) https://doi

Reference 24

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This paper cites Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations

Reference 25

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This paper cites Joule7(10), 2191–2194 (2023) https://doi.org/10.1016/j.joule.2023.09.004.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Joule7(10), 2191–2194 (2023) https://doi.org/10.1016/j.joule.2023.09.004

Reference 26

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This paper cites Reuters (2023).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Reuters (2023)

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery (eds.): Advances in Production Management Systems

Reference 29

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This paper cites Sustainability 17(15), 6891 (2025) https://doi.org/10.3390/su17156891.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Sustainability 17(15), 6891 (2025) https://doi.org/10.3390/su17156891

Reference 30

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This paper cites Artificial Intelligence in Geosciences6(2), 100147 (2025) https://doi.org/10.1016/J.AIIG.2025.100147.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Artificial Intelligence in Geosciences6(2), 100147 (2025) https://doi.org/10.1016/J.AIIG.2025.100147

Reference 31

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nano-Micro Letters17(1), 1–30 (2025) https://doi.org/10.1007/s40820-024-01634-8

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Chemical Engineering Journal517, 164419 (2025) https: //doi.org/10.1016/j.cej.2025.164419

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Advanced Functional Materials34(43) (2024) https://doi.org/10.1002/adfm.202307478

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Statistical Software94(8), 1–36 (2020) https://doi.org/10.18637/jss.v094.i08

Reference 38

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Machine Learning Research12, 2825–2830 (2011)

Reference 40

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery In: Coello, C.A.C

Reference 42

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Proceedings - IEEE International Conference on Robotics and Automation, 16459–16466 (2024) https://doi.org/10.1109/ICRA57147.2024.10611468

Reference 43

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Statistics and Computing8(4), 337–346 (1998) https://doi.org/10.1023/A:1008824606259

Reference 44

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Buildings12(12) (2022) https://doi.org/10.3390/buildings12122109

Reference 45

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This paper cites Applied Energy 363, 123042 (2024) https://doi.org/10.1016/j.apenergy.2024.123042.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Applied Energy 363, 123042 (2024) https://doi.org/10.1016/j.apenergy.2024.123042

Reference 46

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Frontiers in Nuclear Engineering1(December), 1–11 (2022) https://doi.org/10.3389/fnuen.2022.1083164

Reference 47

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Observation 2dd2d418-7067-41e5-af9b-743272420cd7 · outbound

This paper cites Chemistry of Materials 36(22), 11109–11118 (2024) https://doi.org/10.1021/acs.chemmater.4c01978.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Chemistry of Materials 36(22), 11109–11118 (2024) https://doi.org/10.1021/acs.chemmater.4c01978

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.761112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fcd7ba4-24ac-4746-b442-18a094004532 · outbound

This paper cites Automation in Construction170, 105943 (2025) https://doi.org/10.1016/j.autcon.2024.105943.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Automation in Construction170, 105943 (2025) https://doi.org/10.1016/j.autcon.2024.105943

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.763628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bdea29-d5ca-4a95-9085-3a57050987a5 · outbound

This paper cites Chemistry of Materials30(15), 5069–5086 (2018) https://doi.org/ 10.1021/acs.chemmater.8b01425.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Chemistry of Materials30(15), 5069–5086 (2018) https://doi.org/ 10.1021/acs.chemmater.8b01425

Reference 50

Resolution: verified exact; doi, observed 2026-08-03T11:23:28.356638Z

Source-reported events for the cited work

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

Observation 09ba2229-55ab-4a56-aa71-4411a2531ee3 · outbound

This paper cites Scientific Data1, 1–7 (2014) https://doi.org/10.1038/sdata.2014.22.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Scientific Data1, 1–7 (2014) https://doi.org/10.1038/sdata.2014.22

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.768862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b672537d-1f6d-456b-8cfd-bfade59654c1 · outbound

This paper cites Nature Computational Science5(April) (2025) https://doi.org/10.1038/ s43588-025-00777-x.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nature Computational Science5(April) (2025) https://doi.org/10.1038/ s43588-025-00777-x

Reference 52

Resolution: malformed identifier; no resolver link, observed 2026-08-03T11:21:50.771492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9925a73c-f53d-41e2-a34d-9149bc8607f0 · outbound

This paper cites Nature Computational Science5(September) (2024) https://doi.org/10.1038/s43588-025-00858-x.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nature Computational Science5(September) (2024) https://doi.org/10.1038/s43588-025-00858-x

Reference 53

Resolution: verified exact; doi, observed 2026-08-03T11:23:28.216179Z

Source-reported events for the cited work

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

Observation 05a57e66-06c2-4712-a97e-bacef51c5c3c · outbound

This paper cites an unresolved cited work.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Unresolved cited work

Reference 54

Resolution: verified exact; doi, observed 2026-08-03T11:23:28.106145Z

Source-reported events for the cited work

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

Observation a5443cc3-e1cc-4c86-86a4-d8f819b23768 · outbound

This paper cites elegans biomechanics: an empirical and multi-compartmental in silico modelling study.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery elegans biomechanics: an empirical and multi-compartmental in silico modelling study

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.779811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f52566b2-9a1e-4aca-9cea-2c3efaaabce4 · outbound

This paper cites Advanced Intelligence Discovery (2025).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Advanced Intelligence Discovery (2025)

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.782702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e2ea711-852b-40bc-ae7b-7878788eae75 · outbound

This paper cites Journal of Applied Mechanics47(2), 329–334 (1980) https://doi.org/10.1115/1.3153664.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Applied Mechanics47(2), 329–334 (1980) https://doi.org/10.1115/1.3153664

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.785459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3730924a-20ca-422b-8f6c-4cc0ad94977b · outbound

This paper cites Journal of Composite Materials58(27), 2897–2914 (2024) https://doi.org/10.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Composite Materials58(27), 2897–2914 (2024) https://doi.org/10

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.788262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b20368-41ce-48e8-b6cb-382cc19d2869 · outbound

This paper cites In: Computer Aided Chemical Engineering vol.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery In: Computer Aided Chemical Engineering vol

Reference 59

Resolution: verified exact; doi, observed 2026-08-03T11:23:27.979839Z

Source-reported events for the cited work

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

Observation a93507db-a340-4686-b0f2-156d6e97e470 · outbound

This paper cites Nature Reviews Materials6, 201–206 (2021) https://doi.org/10.1038/ s41578-021-00284-1 37.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nature Reviews Materials6, 201–206 (2021) https://doi.org/10.1038/ s41578-021-00284-1 37

Reference 60

Resolution: malformed identifier; no resolver link, observed 2026-08-03T11:21:50.793623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7e06195-096d-4eca-9fc7-916c6ef4cd44 · outbound

This paper cites Nature Physics16(6), 631–635 (2020).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Nature Physics16(6), 631–635 (2020)

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.796570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ad11d6-5463-4f23-b07e-6a13f6691974 · outbound

This paper cites Materials Advances 2(17), 5542–5559 (2021).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Materials Advances 2(17), 5542–5559 (2021)

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.799298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86e081eb-91d6-46c4-9164-01c38f6d803d · outbound

This paper cites Computa- tional Materials Science261, 114270 (2026) https://doi.org/10.1016/j.commatsci.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Computa- tional Materials Science261, 114270 (2026) https://doi.org/10.1016/j.commatsci

Reference 63

Resolution: malformed identifier; no resolver link, observed 2026-08-03T11:21:50.802091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 625e9a26-4159-49fe-a290-f5cb808132c0 · outbound

This paper cites Magnetism3(3), 245–258 (2023).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Magnetism3(3), 245–258 (2023)

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.804727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36b2028d-9128-45d7-91bb-5f90a0b00ea6 · outbound

This paper cites APL Materials12(1) (2024).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery APL Materials12(1) (2024)

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.807362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a263799b-aaef-44d8-93b8-bd1b69bf91b1 · outbound

This paper cites Trends in Chemistry3(5), 342–358 (2021).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Trends in Chemistry3(5), 342–358 (2021)

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.810151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b4141c1-527e-4fd4-ba84-f43169d0a2ba · outbound

This paper cites Science374(6571), 1140–1144 (2021).

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Science374(6571), 1140–1144 (2021)

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.812708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19aa89d0-ad30-4b1d-95b1-42dfa139bad6 · outbound

This paper cites Journal of Sound and Vibration537(July), 117222 (2022) https://doi.org/10.1016/j.jsv.2022.117222.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Sound and Vibration537(July), 117222 (2022) https://doi.org/10.1016/j.jsv.2022.117222

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:50.815336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4ae69db-7e9d-4c88-92cf-b1f3664d12df · outbound

This paper cites Journal of Vibration and Control28(21-22), 2969–2983 (2022) https://doi.org/10.1177/ 10775463211026487.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Journal of Vibration and Control28(21-22), 2969–2983 (2022) https://doi.org/10.1177/ 10775463211026487

Reference 69

Resolution: malformed identifier; no resolver link, observed 2026-08-03T11:21:50.818331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c29770-cdd8-4d7a-9445-e22e962d84f2 · outbound

This paper cites Demokritos.

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery Demokritos

Reference 70

Resolution: verified exact; doi, observed 2026-08-03T11:23:27.855817Z

Source-reported events for the cited work

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

Pith citing papers

Observation 35af6e0d-b5d0-4efb-a687-a76003ce6fd6 · inbound

Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery cites this paper.

Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T19:52:16.954202Z

Source-reported events for the cited work

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