Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:11:19.774197Z
Paper Citation Record · LEDGER
Modeling of Core Loss Based on Machine Learning and Deep Learning
As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2502.05487.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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
Reference resolution
25 of 25 outbound references displayed
- verified exact1
- verified fuzzy22
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 771bb2bd-1d91-4a51-91f5-54595363bf24 · outbound
This paper cites On size and magnetics: Why small efficient power inductors are rare,.
Modeling of Core Loss Based on Machine Learning and Deep Learning On size and magnetics: Why small efficient power inductors are rare,
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:11:19.665437Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70d38e62-d926-4a71-a750-21034837d4e9 · outbound
This paper cites Ferrite core loss for power magnetic components design,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Ferrite core loss for power magnetic components design,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.217329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c67750bc-5340-4ddf-8e1f-a19e5f63963d · outbound
This paper cites A novel calorimetric loss measurement method for high-power high-frequency transformer based on surface temperature measurment,.
Modeling of Core Loss Based on Machine Learning and Deep Learning A novel calorimetric loss measurement method for high-power high-frequency transformer based on surface temperature measurment,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.203605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c11b9d8d-5eb8-4d40-997d-bbdb327f2ba2 · outbound
This paper cites Opportunities and challenges in very high frequency power conversion,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Opportunities and challenges in very high frequency power conversion,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.189245Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0d7f541c-8589-4fea-aa47-ff3d043f7989 · outbound
This paper cites Core loss model for non-sinusoidal excitataions based on vector magnetic circuit theory,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Core loss model for non-sinusoidal excitataions based on vector magnetic circuit theory,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.173771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 290bc6c1-662d-408f-be90-0b3e5a09e007 · outbound
This paper cites High-speed electric machines: Challenges and design considerations,.
Modeling of Core Loss Based on Machine Learning and Deep Learning High-speed electric machines: Challenges and design considerations,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.158069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 37e088b0-509b-41a1-ba95-6f3f4c01507b · outbound
This paper cites Analytical loss model for magnetic cores based on vector magnetic circuit theory,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Analytical loss model for magnetic cores based on vector magnetic circuit theory,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.142380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d6a629f5-40f4-48c6-b232-1113ed8bf0b6 · outbound
This paper cites On the law of hysteresis,.
Modeling of Core Loss Based on Machine Learning and Deep Learning On the law of hysteresis,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.126739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8074b2a5-0d06-4ffc-b005-b9e14b085a4a · outbound
This paper cites Accurate prediction of ferrite core loss with nonsinusoidal waveforms using only steinmetz parameters,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Accurate prediction of ferrite core loss with nonsinusoidal waveforms using only steinmetz parameters,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.111807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2f7cb90e-0629-4b7b-8554-867b5d169dc8 · outbound
This paper cites Improved calculation of core loss with nonsinusoidal waveforms,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Improved calculation of core loss with nonsinusoidal waveforms,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.097049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 316f543e-a75f-42a7-a12e-9f55beada79a · outbound
This paper cites Improved core- loss calculation for magnetic components employed in power electronic systems,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Improved core- loss calculation for magnetic components employed in power electronic systems,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.082190Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 00d32d69-f0ef-4a49-9fa9-d58fcd9c4fcc · outbound
This paper cites Calculation of losses in ferro- and ferrimagnetic materials based on the modified steinmetz equation,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Calculation of losses in ferro- and ferrimagnetic materials based on the modified steinmetz equation,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.067154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 56d5e5c1-16b1-42a4-a0c2-d9cbc253be23 · outbound
This paper cites Calculating core losses in transformers for arbitrary magnetizing currents a comparison of different approaches,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Calculating core losses in transformers for arbitrary magnetizing currents a comparison of different approaches,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.052471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ac3a1983-2a5b-4ba8-abdc-e45919e21140 · outbound
This paper cites Core power losses estimation of wound core distribution transformers with support vector machines,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Core power losses estimation of wound core distribution transformers with support vector machines,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.036971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8d551b03-97ae-4515-b35c-041629c1100c · outbound
This paper cites A hybrid data-driven approach in magnetic core loss modeling for power electronics applications,.
Modeling of Core Loss Based on Machine Learning and Deep Learning A hybrid data-driven approach in magnetic core loss modeling for power electronics applications,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.022287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 0120891f-de78-4ce6-bea9-d041b501aacc · outbound
This paper cites Fabrication-specific simulation of mn-zn ferrite core-loss for machine learning-based surrogate modeling with limited experimental data,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Fabrication-specific simulation of mn-zn ferrite core-loss for machine learning-based surrogate modeling with limited experimental data,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:20.006186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 294e9454-651a-43dd-85b3-2ed8e867a69c · outbound
This paper cites High-frequency core loss modeling based on knowledge-aware artificial neural network,.
Modeling of Core Loss Based on Machine Learning and Deep Learning High-frequency core loss modeling based on knowledge-aware artificial neural network,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.990397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4e254ab7-5361-4a57-b854-52be6763e80f · outbound
This paper cites A core loss estimation method based on data-driven technology with multi- head attention mechanism,.
Modeling of Core Loss Based on Machine Learning and Deep Learning A core loss estimation method based on data-driven technology with multi- head attention mechanism,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.974964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 44cd0a5a-0d9e-4554-b74f-c4d57c098939 · outbound
This paper cites Deep neural network for mag- netic core loss estimation using the magnet experimental database,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Deep neural network for mag- netic core loss estimation using the magnet experimental database,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.956488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 9c7376d1-5e74-4164-9e6e-28c4fe40c0a1 · outbound
This paper cites Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.940622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3b015f54-5d7d-41b1-b511-7d016464e8c0 · outbound
This paper cites How magnet: Machine learning framework for modeling power magnetic ma- terial characteristics,.
Modeling of Core Loss Based on Machine Learning and Deep Learning How magnet: Machine learning framework for modeling power magnetic ma- terial characteristics,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.925814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ea534789-2486-4ac0-be0d-968e2c6a87ee · outbound
This paper cites Physics-inspired multimodal feature fusion cascaded networks for data-driven magnetic core loss modeling,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Physics-inspired multimodal feature fusion cascaded networks for data-driven magnetic core loss modeling,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.910801Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 99433667-844f-4708-bb2f-e4d4d511c08b · outbound
This paper cites Magnet: An open- source database for data-driven magnetic core loss modeling,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Magnet: An open- source database for data-driven magnetic core loss modeling,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T19:11:19.895992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f36f565b-c2ef-496b-ad4f-5330e3eedab2 · outbound
This paper cites Random forests,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Random forests,
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:11:19.770038Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6043be6b-19f3-40bc-b6e1-a18111c10644 · outbound
This paper cites Xgboost: A scalable tree boosting system,.
Modeling of Core Loss Based on Machine Learning and Deep Learning Xgboost: A scalable tree boosting system,
Reference 25
- Resolution
- verified exact
- arxiv_id_nonexistent, observed 2026-08-08T19:11:19.869104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.