Pith. sign in

Paper Citation Record · LEDGER

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning

As of 21 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2505.01531.

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

pith.paper-citation-record.v1
2505.01531 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:22:11.385270Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

58 of 58 outbound references displayed

  • verified exact8
  • verified fuzzy10
  • unresolved38
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d8bb54a0-1109-4a38-ba1f-dac59851c2c8 · outbound

This paper cites write newline.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.040830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.040830Z digest=sha256:178a13d7895454eb95954596141ccbd33580c838c3c1b7fedb6e702f036c6b79

Observation 5a071fb1-9fbf-427e-ad90-2829ac1172e8 · outbound

This paper cites write newline.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning write newline

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.048157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.048157Z digest=sha256:e41e0b83c80580b3bd08d2518addbffdcb303c835706f1efd94cc8e7bad2d6ee

Observation 2e25c245-e491-4103-93ed-b3de9843735b · outbound

This paper cites write newline.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning write newline

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.053972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.053972Z digest=sha256:8ab6bf8252e948bde0a43842ee4310fee1e5c9e372f8d78661898972fa0e175d

Observation 4aca1845-ccac-4dc6-8d00-e645615aa7bc · outbound

This paper cites , author Corrochano, A.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Corrochano, A

Reference 4

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.820222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.060879Z digest=sha256:e95778a002543d6dbabc4c66ec5057c754d096dda25e3d41df537858e6e3c604

Observation cb54a25b-de6d-4ace-b273-16250f16e7a3 · outbound

This paper cites , author Lopez-Martín, M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Lopez-Martín, M

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.817370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.069159Z digest=sha256:969031001141976a1f802c60e6e1eccec5232be6dd18f965c63e8d9c9ab74231

Observation 13ce0e5a-48b7-479d-8e70-ae0b444f1d40 · outbound

This paper cites , author López-Martín, M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author López-Martín, M

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.075919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.075919Z digest=sha256:a72647860f469f048fd25de44fd419a5983bda152b094f9d988e1a623b17eac9

Observation 1dc028b4-02ca-4559-98e5-5c1e34368984 · outbound

This paper cites , year 1991.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 1991

Reference 7

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.802779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.081669Z digest=sha256:d5493c04c9158d1892f0614e2a0fda4c9b8b00557c978d98beee426dca88d723

Observation 2d702085-6468-4100-8afe-ad97fa05190b · outbound

This paper cites Conformal Prediction for Time Series with Modern Hopfield Networks.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning Conformal Prediction for Time Series with Modern Hopfield Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.087915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.087915Z digest=sha256:d77ce1a1ebd84b71860cba53ab9bb9766be434709bc4c06a05eb50ea3188c475

Observation 0c29d8b9-5809-4a10-9ace-ea9c0d73323a · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning xLSTM: Extended Long Short-Term Memory

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.094306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.094306Z digest=sha256:5b8aaa96526f313ca639af191f87f041fedaef43a856dde6407df7cf33c96cea

Observation c50feaa2-e318-4b22-bf81-dc676487a66e · outbound

This paper cites , author Clainche, S.L.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Clainche, S.L

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.796661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.100869Z digest=sha256:e30d5a8d4eb8a22978dc32c5daae4bcdbfb7a1565f9da6a8981d016d81f6c496

Observation 87bc0230-c8b4-4bb3-bc2d-85ffe675670d · outbound

This paper cites , author Le Clainche, S.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Le Clainche, S

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.106144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.106144Z digest=sha256:f769a3a2ee27b5cd2174227c1b96d8ee2b14fc79626b421eee0672424fb8ab51

Observation 2459303d-068b-4313-bc4a-5ea4b4e3d3ec · outbound

This paper cites , author Girfoglio, M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Girfoglio, M

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.112682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.112682Z digest=sha256:db28561c949e96aeae1da6a3c04f7c4a180856a0b699b49391f4f60781ea52f4

Observation 190529e7-a1c3-4efc-8b8a-2fb3047b32f3 · outbound

This paper cites , author Jordan, P.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Jordan, P

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.118440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.118440Z digest=sha256:627b4a79e7209034523173bcd34a9804a45751083ac34051ac6efde6eace54c0

Observation 76ba73b2-e388-42f7-856a-85a3e0cf2fc4 · outbound

This paper cites , author Barone, M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Barone, M

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.123986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.123986Z digest=sha256:89c5a60d1862d9aebadb7dd84dafc2ed76ed1d15ef2a0f2a945e7467698afde5

Observation d4fec4bb-b0df-4098-95ee-40c7d07a8df6 · outbound

This paper cites A predictive physics-aware hybrid reduced order model for reacting flows.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning A predictive physics-aware hybrid reduced order model for reacting flows

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.129602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.129602Z digest=sha256:dd11dd584b5c3cd2e560491aee8641ff47f3c74e45d7791546696d6d93e6d1da

Observation c99c43a8-0b62-4747-a65b-3a42d56bbefb · outbound

This paper cites , author Jati, A.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Jati, A

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.136526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.136526Z digest=sha256:4a84adeb81a57cd72d960ddee3eecc790157c9594b13c568cccb3ba482fe1ab7

Observation 281f62ab-6de1-4eda-bcdf-b4e03852af0d · outbound

This paper cites , author Kaltenbach, S.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Kaltenbach, S

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.143902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.143902Z digest=sha256:960a1cbb4211960bc61f9154395247ccb660b234d8484bad80e2ff1d9f0bd7e6

Observation bb777a51-e96c-4d4b-bd9b-bd597a27239c · outbound

This paper cites , author Bengio, Y.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Bengio, Y

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.774663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.149344Z digest=sha256:e48832656ffce38423a94ff642869ab58314b6e7653881cdfa5b73af25ab1bef

Observation 714d0b70-b497-42f9-810e-07c1d0efc2b3 · outbound

This paper cites , author Bengio, Y.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Bengio, Y

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.155529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.155529Z digest=sha256:c3528b6add132f9289d4e06790f73b981fa81b1bf2f313b3d524b44a97644326

Observation 79cec448-3ea9-4b39-9dc7-01ff45d180aa · outbound

This paper cites , author Wang, Y.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Wang, Y

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.160951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.160951Z digest=sha256:09c536c89f6a6575e94a2d5cb5e062bc333a55e65b64d53ad0dabbcf8119254a

Observation 73336634-6602-42be-a321-43fdf2260830 · outbound

This paper cites , author Fukami, K.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Fukami, K

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.166263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.166263Z digest=sha256:f548b379933fef59822e988d31f64f0131c7017ca3304a8022ec4d4fce46852e

Observation 536a6161-8579-410c-be7e-3f46be3aac45 · outbound

This paper cites , author Zhang, X.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Zhang, X

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.172291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.172291Z digest=sha256:627734b81f534965be0886b077f4846699a37c28b0ae86dfcbdc9fc1535539c2

Observation 4856acbf-2d63-40c9-a37a-e3dd931ef6c7 · outbound

This paper cites , author Schmidhuber, J.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Schmidhuber, J

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.178927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.178927Z digest=sha256:9ff0f0ed80f6d2d41dc2ba24fc532c3a006e3edbf309c726a05b10cb173a9ef7

Observation 7f957835-7d96-45e5-9e77-f99ba49c426e · outbound

This paper cites , author Lumley, J.L.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Lumley, J.L

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.741280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.184365Z digest=sha256:e501d0767e2903e18200f8bc04438e35b5fdba418d834396985b25a0e1499440

Observation d262d7db-00ae-4efc-94c8-cd3b37296cc4 · outbound

This paper cites , year 2018.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 2018

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.190211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.190211Z digest=sha256:c170549a6e0b5d28780a1efaf8dc2c6b0a6f14038f5711a4c5ba1c6a14d08bdb

Observation d74a6a60-10b1-44bb-b84c-11c58f022ccb · outbound

This paper cites Adam: A Method for Stochastic Optimization.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning Adam: A Method for Stochastic Optimization

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.195688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.195688Z digest=sha256:1edc26bb00dd139955adf215cb6dd420eaa916b210e8f3408fc88d25f6151460

Observation 252bc600-d536-4ec0-9e8e-350a0059cc62 · outbound

This paper cites , author Vlachas, P.R.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Vlachas, P.R

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.201787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.201787Z digest=sha256:3997675bbdd821d3383095fb1087b1d88db2939ea9b64f4844996ce2799e885d

Observation cc9959a4-63e0-44d3-9325-af2e0160199b · outbound

This paper cites o wer, M. , author Lottes, J. , author Rasp, S. , author D \.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning o wer, M. , author Lottes, J. , author Rasp, S. , author D \

Reference 28

Resolution
malformed identifier
no resolver link, observed 2026-08-16T04:22:11.208488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.208488Z digest=sha256:0fa1bf01329db8c4e639f71dbe4be337b82b7f0871da7c46e0ae25e0ca3a5937

Observation bcb8afa6-557f-4c2c-80f3-6c03f9a41aa6 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.215580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.215580Z digest=sha256:0c3a07f2423330d2664e1a8940122d971aa516b6e6b73ed6891bf340cf67bd41

Observation 284ac99c-722f-4f05-8dd8-29c80803c9b2 · outbound

This paper cites , author Pérez, J.M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Pérez, J.M

Reference 30

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.662069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.220896Z digest=sha256:7362ee76dbb0e8d26f9aaa52bc0db93a4aeec88ed2c0691f3147cf35399dcab0

Observation b0f72eb1-5f5d-4f99-9ce3-ec0baf3c77c7 · outbound

This paper cites , author Varas, F.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Varas, F

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.226947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.226947Z digest=sha256:cd24b80374b2e3555f030387240cebd6ada5413b76b8fea273e10e80e4906b80

Observation 0e168f7e-bc0d-44c3-a4e9-619516e9c4d7 · outbound

This paper cites , author Vega, J.M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Vega, J.M

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.232064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.232064Z digest=sha256:cf9f09285d453e685bf8e662046f038a3040c602844c73d9a2fac9f7d7de8237

Observation e4967eda-6cad-417e-b3d7-8f70e254c0ab · outbound

This paper cites , author Li, Z.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Li, Z

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.237481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.237481Z digest=sha256:7e22ded5b34f95900262d2ea3f52b6b187a6f3af73b3f13eb358f2485af7b7ac

Observation 50157b22-6766-4a76-8894-cd8c9572070f · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.242634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.242634Z digest=sha256:000730b77a828352f10b291474ed667d8b5aa521c4693515fff7afd4d50e3bcf

Observation 237c880f-d8c0-4d1e-94c6-3cbc7e9acbe4 · outbound

This paper cites , author Noack, B.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Noack, B

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.720417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.247848Z digest=sha256:b2c358e4ce7b67783a5de2d71cce6fbcbe4e243d5984116e889e2eece1e9b5f5

Observation bf7bffe6-f456-435b-aa2d-3983e08e5e48 · outbound

This paper cites , year 1967.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 1967

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.697940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.253273Z digest=sha256:89853cf6dd4a4ed80051c3addb2cb274ca7f5aee4724c8c219d0beaaec9992c5

Observation c1097cee-0b64-43ad-8820-d34fcb0a9ae0 · outbound

This paper cites , author Spiliotis, E.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Spiliotis, E

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.258404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.258404Z digest=sha256:58e15be81b30ef0945adb6a258e5cc078de4f53e8d99d84afad3c1eeb0f92f45

Observation e5538b4b-951d-411c-afac-d86863a991cc · outbound

This paper cites , author López, E.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author López, E

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.263955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.263955Z digest=sha256:04cd56c510bc96c5ad12dcaa2dc71b2b35cabb4c18f730d0479512511b3f89d8

Observation 3dfdd912-d68d-4f30-8966-97126b94fc04 · outbound

This paper cites , author Hess, D.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Hess, D

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.269718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.269718Z digest=sha256:62a724149655269f18037854706ebac5e87e4265b4a75379b2a75592b1c60064

Observation 907e0ab6-693c-4ec8-a6d5-0b4ee5e86754 · outbound

This paper cites A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.275561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.275561Z digest=sha256:94bb1594a5ec52c9b7598e8b79ebda1b16910577e0e38044cebbc2e01c8d06eb

Observation 91ae056c-5b15-422c-afcd-30cb58ab32bd · outbound

This paper cites , author Fukami, K.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Fukami, K

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.282115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.282115Z digest=sha256:a5b5bb813e471d167b33b043ec966bbf86391b26c011e6ee342850778cd30dc0

Observation ceffe8bc-b60b-4a6a-b49b-7910f68d51bc · outbound

This paper cites , author Morzynski, M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Morzynski, M

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.677273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.288581Z digest=sha256:65426a32c1520422b563d5575ecbc4987acb8ce6dad06f358117a02ea41dc0f8

Observation 96b1d695-6ee2-4896-9bd5-3f2eb0816c98 · outbound

This paper cites , author Carlberg, K.T.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Carlberg, K.T

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.302475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.302475Z digest=sha256:597816880d52eac66cb2e8b1bc1c060951cbfa2e942154edc64ba64b64b56733

Observation 1e7bcc65-e25c-42c4-b92d-c0589a8a2fbd · outbound

This paper cites , author Rahman, S.M.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Rahman, S.M

Reference 45

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.560753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.307792Z digest=sha256:057055f2e62c2c6764a289dd0f78ba2a42232379b1083ac72872644821baabeb

Observation d75a62d2-ab99-48e0-8979-552ffeec2a31 · outbound

This paper cites , author Manzoni, A.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Manzoni, A

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.652909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.313207Z digest=sha256:0fba46b57689f5ea70e2137c59eef6608b8da21389aad20c0ca57267e88c63f4

Observation 79174834-04a0-4093-a8b9-90e8b4519156 · outbound

This paper cites , author Terragni, F.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Terragni, F

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.318180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.318180Z digest=sha256:1d401015c420047dd87bb1be067be54b0cb1b49add37ad1cfbc247ff7d0aec4d

Observation 2c5f828f-386c-4c55-9379-e08b246f4d8f · outbound

This paper cites , author Dedè, L.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Dedè, L

Reference 48

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.527926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.323494Z digest=sha256:23890b35f848363e7cc9cd17a10aecc38954031bc32f129222b6a4b79f83b020

Observation dae8f8e3-1011-44f7-948f-f45df98e3b04 · outbound

This paper cites , author Schulze, P.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Schulze, P

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.328912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.328912Z digest=sha256:34dc948f619dd0120812dd4ac83792e65bdb84292acfe9a5f9dc79ca1eae26b5

Observation b7d81c5c-71b1-4210-8973-5e3a3d508437 · outbound

This paper cites , year 2000.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 2000

Reference 50

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.493406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.334505Z digest=sha256:af1391c51bf1267505db607bad331b362a8f4bf5fd16a2d09f2a490ee377b2b3

Observation 18f0ffa6-2969-4926-9cdc-2360b83ad2bd · outbound

This paper cites Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.340138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.340138Z digest=sha256:a30830e4d8253119b489d172fc2cb875ebee478911a1304875cc20232f1382d1

Observation af220f4d-564e-4a33-9a33-59a3477547bb · outbound

This paper cites , year 1987.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 1987

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.624195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.346189Z digest=sha256:e3629fc26f170c37997bded583056533ef8fbd3bca57e03a7cfbcb88532794b4

Observation 673546e3-a831-4d47-82d1-e6bf200610fc · outbound

This paper cites , author Sapsis, T.P.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Sapsis, T.P

Reference 53

Resolution
metadata mismatch
raw_fallback, observed 2026-08-16T04:22:12.038675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.351208Z digest=sha256:92ed673a91485efbadd7e9cb9b40306e195149a41326c5cb3d95519eebcf3d40

Observation c0e6e477-62fe-4b18-810c-59672d0da6d2 · outbound

This paper cites , year 2001.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 2001

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T04:22:12.603551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.357155Z digest=sha256:ccd3ad14a5c756ed2d933faca7decd82df8b3b7523c5c6f6fff2487cb3a2708d

Observation c6d66b88-a8be-4341-a831-71ef4e692d1d · outbound

This paper cites , author Le Clainche , S.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Le Clainche , S

Reference 55

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.472885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.362233Z digest=sha256:916ac8e1c3f4f3db714a5a968854f6cfc23a7c2c70c189827496f965c2e39892

Observation 8a87a8f0-de8b-4b90-abeb-79f5534c83f0 · outbound

This paper cites , author Byeon, W.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Byeon, W

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.367922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.367922Z digest=sha256:497ccaca7496d759ee53e8de1918fd3ee6552daa7ee02c2cf3e372cc8792cfd6

Observation ae6a22bc-a41c-462e-b5b7-64073379ef1d · outbound

This paper cites , year 2004.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , year 2004

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.373330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.373330Z digest=sha256:6cea29cb50471cf427714ceccdedd32dc515f0f085c745147a2e9962e85185ac

Observation a298a00a-34af-415c-9f51-1a3e8c5b902b · outbound

This paper cites , author Heaney, C.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Heaney, C

Reference 58

Resolution
verified exact
doi, observed 2026-08-16T04:22:11.439608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-16T04:22:11.379602Z digest=sha256:8379e41ff57d1f661a4b76ba678a0524f00e19fb96298b354189d33b1123e003

Observation 845cd02e-2de4-4d4e-a8c1-ee43e9ff606d · outbound

This paper cites , author Zhou, G.

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning , author Zhou, G

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-16T04:22:11.385270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:22:11.385270Z digest=sha256:22dc9fbed323df5b221e60634337ef7673bd4a455a58d2875cbf82074c26cff5

Pith citing papers

No inbound Pith citation observations are available.