Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming | Paper Citation Record

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This paper cites Should we apply bias correction to global and regional climate model data?.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Should we apply bias correction to global and regional climate model data?

Reference 1

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Observation 0ae88fa0-0fce-4a43-8fc2-d18c67c3eb98 · outbound

This paper cites Borchers, B.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Borchers, B

Reference 2

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Observation f3854e0f-b1e9-4eb4-bc70-aca4b23e68d0 · outbound

This paper cites Andres, N.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Andres, N

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.427325Z

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Observation 52f3bece-a523-4899-aedb-466dbc3d50ea · outbound

This paper cites APACrefauthors \ 1977.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1977

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.420519Z

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Observation 1549f95e-2689-4d3b-a703-16b01b47b031 · outbound

This paper cites APACrefauthors \ 1985.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1985

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.459286Z

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Observation 00e4e452-215f-46ed-beda-007c61dfca82 · outbound

This paper cites Tsai, W P.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Tsai, W P

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.413330Z

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Observation aaac8600-0ff7-4b25-80c5-2f71b2bfa67b · outbound

This paper cites APACrefauthors \ 1995.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1995

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.465405Z

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Observation 2a1a55cf-cd64-4e39-8c21-267b7c016b1c · outbound

This paper cites Graham, L.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Graham, L

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.435996Z

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Observation 40791481-9812-4c6b-9f83-31ad3941a3a6 · outbound

This paper cites Goswami, S.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Goswami, S

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.432947Z

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Observation 02630060-b022-40bb-9c51-be17d11fae2e · outbound

This paper cites Yulia, R.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Yulia, R

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.438975Z

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Observation 8c82b21d-0e5d-4007-ab7a-620b3af1067f · outbound

This paper cites DiBiase, R.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming DiBiase, R

Reference 11

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.981327Z

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Observation 55697ebe-9940-4b8e-988e-fc984087a885 · outbound

This paper cites APACrefauthors \ 2008.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2008

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.430068Z

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Observation 6288c316-1cb9-400e-a356-577ca8a2f1b2 · outbound

This paper cites APACrefauthors \ 1775.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1775

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.456335Z

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Observation d54932b4-558c-474b-ac83-69bbf80a044b · outbound

This paper cites APACrefauthors \ 1973.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1973

Reference 14

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.988327Z

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Observation 23142a85-a7b4-43e4-a607-73a24cba6143 · outbound

This paper cites \ White, C M.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ White, C M

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:15:21.011848Z

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Observation b1584685-0145-4853-b7ea-e1504b179535 · outbound

This paper cites \ Rackauckas, C.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Rackauckas, C

Reference 16

Resolution: verified exact; doi, observed 2026-05-23T03:15:21.007323Z

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Observation 1cf96b56-b04e-490c-8f30-defd8c2ffaaa · outbound

This paper cites Beck, H.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Beck, H

Reference 17

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.991353Z

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Observation 9d11f102-0f95-44e9-9597-7cf6d9cde0b4 · outbound

This paper cites \ Maurel, F.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Maurel, F

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.518547Z

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Observation edcc17a6-7a08-4be1-93bb-ec3ddb069ab5 · outbound

This paper cites an unresolved cited work.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-23T03:15:21.554884Z

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Observation 6e40849c-c9bd-4632-a40c-c43bb5ae871a · outbound

This paper cites APACrefauthors \ 2015.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2015

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.563785Z

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Observation a51c7a8a-9c83-46e8-a0e1-4b0f1c2c9403 · outbound

This paper cites \ Syme, B.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Syme, B

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.462374Z

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Observation f6ebbe3d-864c-46a0-a551-c945941522de · outbound

This paper cites APACrefauthors \ 2016.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2016

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.445292Z

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Observation 79833481-4978-4f8e-96d1-62692a984799 · outbound

This paper cites Zheng, Y.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Zheng, Y

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.541572Z

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Observation d2f8da5f-2ac8-4c24-b469-c4e63d6b5a98 · outbound

This paper cites APACrefauthors \ 2010.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2010

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.544405Z

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Observation 1897b783-59ae-4306-95d9-0620610d7327 · outbound

This paper cites Ghorbanidehno, H.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Ghorbanidehno, H

Reference 25

Resolution: verified exact; doi, observed 2026-05-23T03:15:21.017020Z

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Observation 1961071a-658a-4ed3-ab5a-15bf17b3e48d · outbound

This paper cites torchode: A Parallel ODE Solver for PyTorch.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming torchode: A Parallel ODE Solver for PyTorch

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:15:21.190329Z

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Observation ed5adcf8-99f5-4172-95d1-aac31b932694 · outbound

This paper cites APACrefauthors \ 1970.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1970

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.538096Z

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Observation 516ba153-c648-40cd-b9af-f7c16932adf7 · outbound

This paper cites APACrefauthors \ 2025.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.532212Z

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Observation efeb5900-542c-4782-9a63-e83ea8ba2b0c · outbound

This paper cites Landry, B J.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Landry, B J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.529507Z

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Observation f01586c8-c4cc-4e9d-a43d-a68ee6cc1098 · outbound

This paper cites Mazdeh, A.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Mazdeh, A

Reference 30

Resolution: malformed identifier; doi_truncated, observed 2026-05-23T03:15:21.014414Z

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Observation 7bd449b6-0ab5-404f-a4e9-14fddf2af076 · outbound

This paper cites Shen, C.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Shen, C

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.534881Z

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Observation adca9e34-af6f-41af-af2d-e989e37ab957 · outbound

This paper cites APACrefauthors \ 1891.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1891

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.547759Z

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Observation 693cd0cf-2af1-45a8-b2bc-4385bcf75ad7 · outbound

This paper cites Roberts, K.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Roberts, K

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.557606Z

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Observation ee55a542-3e91-4471-b4e2-0bd7bc8188c6 · outbound

This paper cites Pereira, F.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Pereira, F

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.448036Z

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 4afc2e4f-418f-478b-a538-2f0a7644c59d · outbound

This paper cites Saki, S.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Saki, S

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.442028Z

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Observation 098b485b-628a-4c98-a4eb-e742ebaf4b9b · outbound

This paper cites APACrefauthors \ 1944.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1944

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.523701Z

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Observation 039f83d5-4049-4d55-88a1-319705d681da · outbound

This paper cites o mungsgesetze in rauhen R ohren Str\.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming o mungsgesetze in rauhen R ohren Str\

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.526693Z

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Observation e4ff40a1-ee66-4247-9089-a5556c586040 · outbound

This paper cites APACrefauthors \ 1950.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1950

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.560371Z

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

Observation 9a09d6b3-aacb-476a-b79f-655b304ec60b · outbound

This paper cites APACrefauthors \ 2011.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2011

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.566735Z

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Observation 5d3d7311-2925-41de-9928-c76f3f3b5317 · outbound

This paper cites Moteki, D.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Moteki, D

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.417051Z

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Observation 891917fe-47d1-4e58-a769-c8b3d75ca2aa · outbound

This paper cites Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:15:21.195369Z

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Observation 1205aebd-a0d1-4d8c-a111-d0ea3a716003 · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Universal Differential Equations for Scientific Machine Learning

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-23T03:15:21.184661Z

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Observation 832277e0-acee-4af3-b6bd-120f1067c5ed · outbound

This paper cites \ Nie, Q.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Nie, Q

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.552162Z

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Observation 297a4183-cc48-4978-a16c-0f6d7c38d3c7 · outbound

This paper cites Appling, A.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Appling, A

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.515279Z

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Observation 7e9884e8-4723-4561-ab67-068695d7956e · outbound

This paper cites Perdikaris, P.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Perdikaris, P

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.521206Z

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Observation 0cdf88af-d2a0-4a00-abdd-ca6b8b62d6d0 · outbound

This paper cites Borthwick, A G.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Borthwick, A G

Reference 46

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.984437Z

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Observation 0a490ab9-b5bb-4ab0-a5f9-429c3e79263c · outbound

This paper cites Fujihara, M.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Fujihara, M

Reference 47

Resolution: verified exact; doi, observed 2026-05-23T03:15:21.004355Z

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Observation 9554b513-6323-41f8-a659-f2c1d9395cfc · outbound

This paper cites APACrefauthors \ 1965.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1965

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.506538Z

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Observation 2c81961f-407b-4f40-ab56-6959d179b4e5 · outbound

This paper cites Bolibar, J.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Bolibar, J

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.453624Z

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Observation 0bbee144-3028-4172-a4f2-f2e58852d40a · outbound

This paper cites APACrefauthors \ 1991.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1991

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.503673Z

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 2cb204f2-a79e-4634-992f-3acebabfcc6b · outbound

This paper cites Appling, A P.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Appling, A P

Reference 51

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.994027Z

Source-reported events for the cited work

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Observation 9d86da7b-4870-4213-8a02-94bc4fc06ec5 · outbound

This paper cites \ Phanikumar, M S.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Phanikumar, M S

Reference 52

Resolution: verified exact; doi, observed 2026-05-23T03:15:20.997972Z

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Observation a9351110-c0b4-4f89-a827-e1684c47c930 · outbound

This paper cites Le Maitre, O.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Le Maitre, O

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.497977Z

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Observation 08392882-40ba-4265-8a7b-4810354f3e2f · outbound

This paper cites Mayo, T.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Mayo, T

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.450794Z

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

Observation 005bd7c1-a60f-411a-8606-4fa155420a2e · outbound

This paper cites Bindas, T.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Bindas, T

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.495139Z

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 41054173-4f71-40e5-bbaa-d31887cf2f5b · outbound

This paper cites Knoben, W J.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Knoben, W J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.500768Z

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 5606f130-33f6-494e-bf01-40d035d67a3b · outbound

This paper cites Sawadekar, K.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Sawadekar, K

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.509479Z

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 d43a5d90-4bd3-40c3-a9ab-7da2ff2a0f80 · outbound

This paper cites Shen, C.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Shen, C

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.512366Z

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 c524920d-c9bf-4256-b576-23042f2334ef · outbound

This paper cites APACrefauthors \ 2022.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2022

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.489693Z

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 5e36ca4f-3087-440f-97a3-9612a2e625ba · outbound

This paper cites Runge–Kutta pairs of order 5(4) satisfying only the first column simplifying assumption.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Runge–Kutta pairs of order 5(4) satisfying only the first column simplifying assumption

Reference 60

Resolution: metadata mismatch; doi, observed 2026-05-23T03:15:21.001414Z

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 fbf0fe18-243b-4c75-bed7-3fa1257d3095 · outbound

This paper cites Younis, J.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Younis, J

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.483350Z

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 02d0436c-fdd1-477f-803b-f4627be7d0da · outbound

This paper cites Jiang, S.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Jiang, S

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.486378Z

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 0599f1cd-af4a-4449-9e44-61b94d13bb19 · outbound

This paper cites \ Bach, H.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming \ Bach, H

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.492310Z

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 2580bd8a-44bc-411f-96f0-6168a049dc3d · outbound

This paper cites Zhang, J.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Zhang, J

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.477650Z

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 ef149a80-d9ce-427c-baec-e38a8d6c4526 · outbound

This paper cites Lehman, W.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Lehman, W

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.480531Z

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 0b7b363b-ab2d-4a63-b593-ad66c3aab484 · outbound

This paper cites APACrefauthors \ 1992.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 1992

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.471572Z

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 0aee79c0-c52a-4330-b378-262066cdedc7 · outbound

This paper cites APACrefauthors \ 2002.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming APACrefauthors \ 2002

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.468403Z

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 938bb49a-96a3-4f10-9011-7b3875e8a00b · outbound

This paper cites Lei, H.

Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming Lei, H

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:15:21.474846Z

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.

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