Paper Citation Record · LEDGER

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models

As of 7 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2505.20930.

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

pith.paper-citation-record.v1
2505.20930 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:50:54.456557Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

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verified fuzzy17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 97485ea0-70da-496a-a8ed-ffc03e54b08d · outbound

This paper cites Uncertainty characterization for generation adequacy assessments – including an application to the recent european energy crisis,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Uncertainty characterization for generation adequacy assessments – including an application to the recent european energy crisis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:58.538568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d5ce085-c705-44ba-8fb8-b331c456765a · outbound

This paper cites Clarifying the inter- pretation and use of the LOLE resource adequacy metric,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Clarifying the inter- pretation and use of the LOLE resource adequacy metric,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:58.375727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b5c2fd7-d8c1-4d28-955d-a96b6b3153b4 · outbound

This paper cites Minimizing unserved energy using heterogeneous storage units,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Minimizing unserved energy using heterogeneous storage units,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:58.204586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78f74464-8c64-4ce7-9285-a66e252dbcfa · outbound

This paper cites Assessment of the ca- pacity credit of renewables and storage in multi-area power systems,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Assessment of the ca- pacity credit of renewables and storage in multi-area power systems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:58.051116Z

Source-reported events for the cited work

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Observation a2ae5ea1-c7ac-44c4-a3c9-65a88671803c · outbound

This paper cites Surrogate model uncertainty quantification for reliability-based design optimization,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Surrogate model uncertainty quantification for reliability-based design optimization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:57.910724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1fc95584-b44d-44e9-85a9-7046219ae7cc · outbound

This paper cites An intelligent operational reliability assessment approach considering sample imbalance,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models An intelligent operational reliability assessment approach considering sample imbalance,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:57.760135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e5340b3c-8848-4578-a0d9-9e8aa00c2c38 · outbound

This paper cites Multilevel monte carlo for reliability theory,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Multilevel monte carlo for reliability theory,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:57.544365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c92868e9-0764-45d1-af9d-35f8b3c2644f · outbound

This paper cites Multilevel monte carlo with surrogate models for resource adequacy assessment,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Multilevel monte carlo with surrogate models for resource adequacy assessment,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:57.350189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63406e6b-466f-4aaa-a240-b3d50cc53c11 · outbound

This paper cites Deep-learning-enhanced static risk- oriented security assessment under uncertainty,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Deep-learning-enhanced static risk- oriented security assessment under uncertainty,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:57.060298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca7d680b-3f55-41ca-ba83-2ce1b2264331 · outbound

This paper cites Artificial intelligence and design of experiments for resource adequacy assessment in power systems,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Artificial intelligence and design of experiments for resource adequacy assessment in power systems,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:56.628082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a7e81d5-8194-4d6d-9b42-c0d333e36033 · outbound

This paper cites Accelerating system adequacy assess- ment using the multilevel monte carlo approach,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Accelerating system adequacy assess- ment using the multilevel monte carlo approach,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:56.350453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c6371fe-27dc-4274-a9cf-b201a693105f · outbound

This paper cites Surrogate model assisted multi- criteria operation evaluation of community integrated energy sys- tems,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Surrogate model assisted multi- criteria operation evaluation of community integrated energy sys- tems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:56.054052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f30ee66a-d0fa-471b-85d2-82a22486990a · outbound

This paper cites Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:55.714657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64664dd4-9034-417e-84e4-78403e3872e3 · outbound

This paper cites Active learning literature survey,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Active learning literature survey,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:55.540467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b8bc951d-383e-4014-a96c-6c156fbe41a5 · outbound

This paper cites Building energy optimiza- tion using surrogate model and active sampling,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Building energy optimiza- tion using surrogate model and active sampling,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:55.326978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 24490493-da50-44cf-829f-1041a827dc08 · outbound

This paper cites Active learning-based machine learning approach for enhancing environmental sustainability in green building energy con- sumption,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Active learning-based machine learning approach for enhancing environmental sustainability in green building energy con- sumption,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:55.023145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0cdd1fba-3a17-46c0-8014-ab83e86e9f99 · outbound

This paper cites Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability,.

MLMC-based Resource Adequacy Assessment with Active Learning Trained Surrogate Models Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:54.822919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.