Paper Citation Record · LEDGER

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing

As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.01670.

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

pith.paper-citation-record.v1
2607.01670 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-03T17:59:38.427560Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

40 of 40 outbound references displayed

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verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8cc21ed-d6eb-4ded-8fcf-303b57a7e737 · outbound

This paper cites Chronos-2: From Univariate to Universal Forecasting.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Chronos-2: From Univariate to Universal Forecasting

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-03T18:08:45.600490Z

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 1faea7f7-5f8c-485d-b180-0c2b9e66d384 · outbound

This paper cites Improving day-ahead solar irradiance time series forecasting by leveraging spatio-temporal context.NeurIPS, 36:2342–2367, 2023.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Improving day-ahead solar irradiance time series forecasting by leveraging spatio-temporal context.NeurIPS, 36:2342–2367, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.844797Z

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 70161e45-67ab-48cf-8a5d-5bd06989d48e · outbound

This paper cites Multistage wind-electric power forecast by using a combination of advanced statistical methods.IEEE Trans.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Multistage wind-electric power forecast by using a combination of advanced statistical methods.IEEE Trans

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.842870Z

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 c8a83fb7-350f-42d2-84f7-abbaa2d4a390 · outbound

This paper cites Multivariate wind power time series forecasting with noise-filtering neural odes.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Multivariate wind power time series forecasting with noise-filtering neural odes

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.846747Z

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 35a40ea9-9b09-4611-8f59-12a27d953c21 · outbound

This paper cites WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T18:08:45.603749Z

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 27f378d4-a038-4caa-99d3-8068ae76c66f · outbound

This paper cites M2gsnet: Multi- modal multi-task graph spatiotemporal network for ultra-short-term wind farm cluster power prediction.Applied Sciences, 10(21):7915, 2020.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing M2gsnet: Multi- modal multi-task graph spatiotemporal network for ultra-short-term wind farm cluster power prediction.Applied Sciences, 10(21):7915, 2020

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.854738Z

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 015e5f77-09c4-4ef2-bc23-616a6b74e98c · outbound

This paper cites Unraveling spatio-temporal foundation models via the pipeline lens: A comprehensive review.IEEE Trans.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Unraveling spatio-temporal foundation models via the pipeline lens: A comprehensive review.IEEE Trans

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.832121Z

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 89af68d8-e0c1-4bcc-83ca-f7be8cccdf7c · outbound

This paper cites Efficient high-dimensional time series forecasting with transformers: A channel reordering perspective.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Efficient high-dimensional time series forecasting with transformers: A channel reordering perspective

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.834263Z

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 5147ac64-ddfc-4c3d-9567-27fde68342f2 · outbound

This paper cites Previento-a wind power prediction system with an innovative upscaling algorithm.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Previento-a wind power prediction system with an innovative upscaling algorithm

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.836252Z

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 a2fe1356-b7ee-402e-a68b-56b41f54d7d1 · outbound

This paper cites wind-python/windpowerlib: Update release, 2024.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing wind-python/windpowerlib: Update release, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.832282Z

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 c64489be-abce-4587-b3c1-4bcf56d4953a · outbound

This paper cites A critical review of wind power forecasting methods—past, present and future.Energies, 13(15):3764, 2020.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A critical review of wind power forecasting methods—past, present and future.Energies, 13(15):3764, 2020

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.823485Z

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 83338c75-1fc6-419d-af04-5f43a3d06b1e · outbound

This paper cites A hybrid deep learning-based neural network for 24-h ahead wind power forecasting.Applied Energy, 250:530–539, 2019.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A hybrid deep learning-based neural network for 24-h ahead wind power forecasting.Applied Energy, 250:530–539, 2019

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.825425Z

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 49048c6d-e465-4b63-82fa-c10b6442b5f2 · outbound

This paper cites A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting.IEEE Access, 7:28309–28318, 2019.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A model combining convolutional neural network and lightgbm algorithm for ultra-short-term wind power forecasting.IEEE Access, 7:28309–28318, 2019

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.827464Z

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 3ec5bd09-388b-4eec-9b57-50d55ca4cb0f · outbound

This paper cites Wind power forecasting based on hybrid ceemdan-ewt deep learning method.Renewable Energy, 218:119357, 2023.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Wind power forecasting based on hybrid ceemdan-ewt deep learning method.Renewable Energy, 218:119357, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.838260Z

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 6d525e08-2b51-4b08-841c-357db79a6a17 · outbound

This paper cites Tackling Climate Change with Machine Learning.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Tackling Climate Change with Machine Learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.852469Z

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 7f944eb6-4eb2-4592-b154-da83b8a1fdc9 · outbound

This paper cites A physical approach of the short-term wind power prediction based on cfd pre-calculated flow fields.Journal of Hydrodynamics, Ser.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A physical approach of the short-term wind power prediction based on cfd pre-calculated flow fields.Journal of Hydrodynamics, Ser

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.821574Z

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 1da6421e-a8aa-4666-b233-d6cc163a9c77 · outbound

This paper cites Solarcube: an integrative benchmark dataset harnessing satellite and in-situ observations for large-scale solar energy forecasting.NeurIPS, 37:3499–3513, 2024.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Solarcube: an integrative benchmark dataset harnessing satellite and in-situ observations for large-scale solar energy forecasting.NeurIPS, 37:3499–3513, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.819180Z

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 48924db2-80c3-4bba-8609-fe4861fa9fc4 · outbound

This paper cites Moirai-moe: Empowering time series foundation models with sparse mixture of experts.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Moirai-moe: Empowering time series foundation models with sparse mixture of experts

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.815148Z

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 a1727fee-dec0-495d-9cb7-26cb03e63933 · outbound

This paper cites itransformer: Inverted transformers are effective for time series forecasting.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing itransformer: Inverted transformers are effective for time series forecasting

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.815104Z

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 37fcf4f8-6de4-4f05-baf8-45a5ac0f2d95 · outbound

This paper cites Improv- ing time series forecasting via instance-aware post-hoc revision.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Improv- ing time series forecasting via instance-aware post-hoc revision

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.816930Z

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 b7217f4b-507b-4fb4-bafc-6ee296f577ec · outbound

This paper cites Fusionsf: Fuse heterogeneous modalities in a vector quantized framework for robust solar power forecasting.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Fusionsf: Fuse heterogeneous modalities in a vector quantized framework for robust solar power forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.811268Z

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 d5954446-dbb6-4408-b4e6-0ced0279c023 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A time series is worth 64 words: Long-term forecasting with transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.858216Z

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 6f9947e0-452b-4775-a5cf-909fef6d02da · outbound

This paper cites A novel wind power forecast model: Statistical hybrid wind power forecast technique (shwip).IEEE Trans.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A novel wind power forecast model: Statistical hybrid wind power forecast technique (shwip).IEEE Trans

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.848647Z

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 f96a9e7d-ab15-4693-9463-41b86eac128e · outbound

This paper cites Gaussian process power curve models incorporating wind turbine operational variables.Energy Reports, 6:1658–1669, 2020.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Gaussian process power curve models incorporating wind turbine operational variables.Energy Reports, 6:1658–1669, 2020

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.796674Z

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 89168fcd-8ddd-4c39-8c72-d28f57c3b115 · outbound

This paper cites A hybrid wind power forecast- ing model with xgboost, data preprocessing considering different nwps.Applied Sciences, 11(3):1100, 2021.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A hybrid wind power forecast- ing model with xgboost, data preprocessing considering different nwps.Applied Sciences, 11(3):1100, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.840546Z

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 dd0aefb5-52b9-4944-9c6f-d7470d92c1a7 · outbound

This paper cites Kelmarsh wind farm data, 2022.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Kelmarsh wind farm data, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.804793Z

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 54ef0aab-1c6e-4fc9-8468-12aa3b8d9569 · outbound

This paper cites Penmanshiel wind farm data, 2022.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Penmanshiel wind farm data, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.808865Z

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 213e4718-23ac-489c-9a1d-625028733e1f · outbound

This paper cites Wind power forecasting for a real onshore wind farm on complex terrain using wrf high resolution simulations.Renewable Energy, 135:674–686, 2019.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Wind power forecasting for a real onshore wind farm on complex terrain using wrf high resolution simulations.Renewable Energy, 135:674–686, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.802005Z

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 f68a7263-b892-4db1-b03a-29caa0b92be0 · outbound

This paper cites An advanced statistical method for wind power forecasting.IEEE Trans.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing An advanced statistical method for wind power forecasting.IEEE Trans

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.784777Z

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 76d9bb8d-a17c-49ae-9727-1bbd9552fe7f · outbound

This paper cites xpatch: Dual-stream time series forecasting with exponential seasonal-trend decomposition.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing xpatch: Dual-stream time series forecasting with exponential seasonal-trend decomposition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.786935Z

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 028e2bc7-cce0-4380-87e5-8aa293093499 · outbound

This paper cites Solarmae: A unified framework for regional centralized and distributed solar power forecasting with weather pre-training.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Solarmae: A unified framework for regional centralized and distributed solar power forecasting with weather pre-training

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.790090Z

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 9582f9d5-cc1f-431c-a187-e8890c7b1e81 · outbound

This paper cites Timemixer: Decomposable multiscale mixing for time series forecasting.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Timemixer: Decomposable multiscale mixing for time series forecasting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.794700Z

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 7f040841-e5a4-4dd4-80f6-c34af0d72cc9 · outbound

This paper cites Lightgts: A lightweight general time series forecasting model.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Lightgts: A lightweight general time series forecasting model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.813215Z

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 b6d47d5e-b05e-4d25-acad-50cb3f63a59a · outbound

This paper cites Short-term wind power forecasting based on clustering pre-calculated cfd method.Energies, 11(4):854, 2018.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Short-term wind power forecasting based on clustering pre-calculated cfd method.Energies, 11(4):854, 2018

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.806896Z

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 15b0b95b-b245-4a7e-869c-dc72445d8861 · outbound

This paper cites Approaches to wind power curve modeling: A review and discussion.Renew.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Approaches to wind power curve modeling: A review and discussion.Renew

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.798492Z

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 3792c17b-d060-4362-99f9-592679f8e58d · outbound

This paper cites Electric-carbon market coupling and price transmission mechanism in china: An empirical analysis and development barriers study.J.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Electric-carbon market coupling and price transmission mechanism in china: An empirical analysis and development barriers study.J

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.850799Z

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 69214b58-1635-41fa-bf9e-744ab9f400cf · outbound

This paper cites A cross- dataset benchmark for neural network-based wind power forecasting.Renewable Energy, 254:123463, 2025.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A cross- dataset benchmark for neural network-based wind power forecasting.Renewable Energy, 254:123463, 2025

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.782763Z

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 42f7a42d-dd1c-46c9-b687-0f43dcceab74 · outbound

This paper cites 2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing 2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-03T18:08:45.602921Z

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 84a7019f-ddf9-4a11-8e72-9a96c9859312 · outbound

This paper cites A novel bidirectional mechanism based on time series model for wind power forecasting.Applied Energy, 177:793– 803, 2016.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing A novel bidirectional mechanism based on time series model for wind power forecasting.Applied Energy, 177:793– 803, 2016

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T04:10:40.780690Z

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 1101d04c-8e90-43d9-8cbe-fa10c8d78d5d · outbound

This paper cites Deep latent state space models for time-series generation.

UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing Deep latent state space models for time-series generation

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-07-05T04:10:40.774968Z

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

No inbound Pith citation observations are available.