Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:35:49.791708Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2411.13361.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:35:49.791708Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
46 of 46 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 7ae072a0-dbf6-452c-b53e-f3de408aee0e · outbound
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Dobrilla, M
Reference 1
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 3
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Girolami, B
Reference 7
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Bierkens, P
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 12
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Ching, Y.-C
Reference 13
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 14
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 16
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Bayesian Inference of Fiber Orientation and Polymer Properties in Short Fiber-Reinforced Polymer Composites
Reference 17
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 18
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 20
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 21
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Reference 22
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Reference 23
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Dasgupta, D
Reference 24
Source-reported events for the cited work
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Kandasamy, J
Reference 25
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Reference 26
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 27
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Reference 28
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Reference 29
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Reference 30
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 31
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 32
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Vigliotti, G
Reference 33
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 34
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 35
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 36
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 37
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 38
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Reference 39
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 40
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Fletcher, Practical methods of optimization, Practical Methods of Optimization (2000)
Reference 41
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Observation 6ec1ef9c-70e8-436c-bbef-200b4cd49a7c · outbound
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 42
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 43
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Observation dd942949-ae3c-4d32-a299-6727712bd915 · outbound
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 44
Source-reported events for the cited work
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Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 1154
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Observation 58ac9c74-91df-4e93-b428-f0f14652929f · outbound
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties Unresolved cited work
Reference 4886
Source-reported events for the cited work
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No inbound Pith citation observations are available.