Hybrid Least Squares/Gradient Descent Methods for MIONets | Paper Citation Record

Observation a27e8b60-f617-4e9e-a185-f0d56c68d197 · outbound

This paper cites Learning nonlinear operators via.

Hybrid Least Squares/Gradient Descent Methods for MIONets Learning nonlinear operators via

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.386388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dad7418f-bc5a-4fc1-b9b8-687e50dbdd16 · outbound

This paper cites IEEE Transactions on Neural Networks , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets IEEE Transactions on Neural Networks , volume=

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.388048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9916e4e-50ac-4a5f-8797-223f0ef53eb0 · outbound

This paper cites Mathematical and Scientific Machine Learning , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Mathematical and Scientific Machine Learning , pages=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.425913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dbe7f4a6-48cd-4ef8-ac22-785d8c35b65f · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed.

Hybrid Least Squares/Gradient Descent Methods for MIONets Learning the solution operator of parametric partial differential equations with physics-informed

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.397635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1bdf3887-ae6f-421c-9ddd-1fbd04e0fcd5 · outbound

This paper cites 2022 , publisher=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 2022 , publisher=

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.383326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 024d32bd-f90a-4fcc-8277-0c6a60dea39c · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Hybrid Least Squares/Gradient Descent Methods for MIONets Adam: A Method for Stochastic Optimization

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T00:45:49.149542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a6645fab-147a-4519-af8d-594360d35c2a · outbound

This paper cites Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , pages=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.411655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46149e43-66a9-497e-8cb3-efbb52bf6c8f · outbound

This paper cites Proceedings of the IEEE International Conference on Computer Vision , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Proceedings of the IEEE International Conference on Computer Vision , pages=

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.376431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f1318910-f9e5-4a17-b80c-d5c7385f89c5 · outbound

This paper cites Mathematical Proceedings of the Cambridge Philosophical Society , author=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Mathematical Proceedings of the Cambridge Philosophical Society , author=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.462332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c87f5846-0e8d-488c-b80d-63545ae597d2 · outbound

This paper cites Matemati.

Hybrid Least Squares/Gradient Descent Methods for MIONets Matemati

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.419530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4557e27-95f3-4a78-92f4-460f7f896267 · outbound

This paper cites Selected Papers Volume I , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Selected Papers Volume I , pages=

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.371694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c6b87f4-88c3-4374-8a0c-98094d379e55 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:45:49.370043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ceba4a70-f642-4c27-acb3-63e833638b4b · outbound

This paper cites Partitioned neural network approximation for partial differential equations enhanced with.

Hybrid Least Squares/Gradient Descent Methods for MIONets Partitioned neural network approximation for partial differential equations enhanced with

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.380006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a1e6b239-7bd9-4c84-be2a-fc96c70442e2 · outbound

This paper cites Magnus and H.

Hybrid Least Squares/Gradient Descent Methods for MIONets Magnus and H

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.395885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a85ff9f2-4b69-409a-bcdd-65314e70605e · outbound

This paper cites , journal=.

Hybrid Least Squares/Gradient Descent Methods for MIONets , journal=

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.465266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e91ac9ce-ba8a-46df-98e2-adc3825a3df0 · outbound

This paper cites The solution of the matrix equations.

Hybrid Least Squares/Gradient Descent Methods for MIONets The solution of the matrix equations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.470514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a794ba80-2f4e-498e-87e6-da6774a3644b · outbound

This paper cites Algorithm 432.

Hybrid Least Squares/Gradient Descent Methods for MIONets Algorithm 432

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.459108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 715597b3-f1da-418c-95b3-404848dd8e4c · outbound

This paper cites Doklady Akademii Nauk , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Doklady Akademii Nauk , volume=

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.457331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e0709e7-07d2-4953-b317-14cd6edb6ffa · outbound

This paper cites SIAM Journal on Matrix Analysis and Applications , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets SIAM Journal on Matrix Analysis and Applications , volume=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.460682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3ed3a2ea-f064-436a-99c1-696a83557c30 · outbound

This paper cites 2009 , school=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 2009 , school=

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.463822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 11611da0-b665-4304-8748-c802aab2d2a2 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:45:49.468378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c1d6957-7a5e-43ab-b27a-32cc6de2edef · outbound

This paper cites Journal of the American Statistical Association , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Journal of the American Statistical Association , volume=

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.447325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8cb02eb7-a417-41aa-bd30-8cb3baa9988a · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Computer Methods in Applied Mechanics and Engineering , volume=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.445626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f14c2af7-beee-48fe-9e26-ca0115c2990f · outbound

This paper cites Mathematics of Control, Signals and Systems , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Mathematics of Control, Signals and Systems , volume=

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.449088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 14527ddf-50e9-4215-9425-6640e84ff5ac · outbound

This paper cites Neural Networks , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Neural Networks , volume=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.453841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4cf57fbc-1a0e-49a9-a424-3746f65feea5 · outbound

This paper cites Applied and Computational Harmonic Analysis , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Applied and Computational Harmonic Analysis , volume=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.437564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48d4d75b-1585-47c9-957d-24682d2c72cc · outbound

This paper cites Refinement and universal approximation via sparsely connected.

Hybrid Least Squares/Gradient Descent Methods for MIONets Refinement and universal approximation via sparsely connected

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.440870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f6df7d4-8a37-4b2c-8a3d-5f87e4539c2c · outbound

This paper cites IEEE Transactions on Information Theory , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets IEEE Transactions on Information Theory , volume=

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.439147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16ae125b-030c-40f7-87d0-6378307c8298 · outbound

This paper cites Neural Networks , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Neural Networks , volume=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.442424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c79b461f-bb5e-4258-a0d0-14c60ef0010a · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Advances in Neural Information Processing Systems , volume=

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.455443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 371d49ab-6b55-470b-9ef1-48fdb66b39a7 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.432784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f42a60dd-7567-47af-8647-5823a0990331 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Advances in Neural Information Processing Systems , volume=

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.434405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1876a6d-1970-4570-940d-e2d1305707fc · outbound

This paper cites International Journal of Neural Systems , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets International Journal of Neural Systems , volume=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.427577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8d6d6ea5-2b94-4eeb-8028-a30030c76181 · outbound

This paper cites 2011 , publisher=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 2011 , publisher=

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.424272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fe111056-c3bd-449e-9796-30d31ac3aaae · outbound

This paper cites A Survey on Universal Approximation Theorems.

Hybrid Least Squares/Gradient Descent Methods for MIONets A Survey on Universal Approximation Theorems

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-09T00:45:49.156566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 993be4f3-9cfd-4df8-a90b-d908484722bf · outbound

This paper cites 2018 , publisher=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 2018 , publisher=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.373282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56bb042b-b079-469a-9074-32ebcd80c0bb · outbound

This paper cites IEEE Signal Processing Letters , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets IEEE Signal Processing Letters , volume=

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.421133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation af2689ea-4ce7-4470-8eeb-b62708e5fee8 · outbound

This paper cites Enhanced line search: A novel method to accelerate.

Hybrid Least Squares/Gradient Descent Methods for MIONets Enhanced line search: A novel method to accelerate

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.430920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 413a05da-23fd-41f6-a83b-ad80b2ab5ad0 · outbound

This paper cites 1997 , publisher=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 1997 , publisher=

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.416429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b18c6ca2-2295-467e-b604-74ea70a96aed · outbound

This paper cites Journal of Computational Physics , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Journal of Computational Physics , volume=

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.414892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 96fce1c9-6e6f-47bf-a8d6-ce001bc6d515 · outbound

This paper cites Journal of Machine Learning Research , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Journal of Machine Learning Research , volume=

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.417990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7cb1cfc0-caee-4331-ba2c-3b57d7bd97b0 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:45:49.435933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f0cfd981-24d9-4dd1-8530-21bf296b1157 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:45:49.450642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c1eac63-0a23-4bba-9048-33b2e9096ce6 · outbound

This paper cites 2016 , organization=.

Hybrid Least Squares/Gradient Descent Methods for MIONets 2016 , organization=

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.399168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ad92f956-0ff5-4645-bc03-680a6dd630f6 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-07-09T00:45:49.403646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 029bf184-c462-434e-b49c-6fabf9e22df9 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Advances in Neural Information Processing Systems , volume=

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.394333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 161cfeea-e74a-4402-a9e5-4fcda181a6fc · outbound

This paper cites , author=.

Hybrid Least Squares/Gradient Descent Methods for MIONets , author=

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.405290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9db8595b-d066-4e26-8740-57b934808f3a · outbound

This paper cites Domain Decomposition Methods in Science and Engineering XXVI , pages=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Domain Decomposition Methods in Science and Engineering XXVI , pages=

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.392734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c2574d0c-41dd-496d-83bc-f03253d2dc3e · outbound

This paper cites Neural Networks , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Neural Networks , volume=

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.391125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2250c648-01d0-4ee7-9eee-993b1357f294 · outbound

This paper cites Physics of Fluids , volume=.

Hybrid Least Squares/Gradient Descent Methods for MIONets Physics of Fluids , volume=

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.384889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1df1a467-752b-4681-86d9-aa0d9229ad8b · outbound

This paper cites Searching for Activation Functions.

Hybrid Least Squares/Gradient Descent Methods for MIONets Searching for Activation Functions

Reference 51

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T00:45:49.151858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ed539fc-a18e-4bcf-98a7-6dc1015d2a4a · outbound

This paper cites On the limited memory.

Hybrid Least Squares/Gradient Descent Methods for MIONets On the limited memory

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.400741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 507558ce-a68a-4d76-b4ae-bd7bd3dfffd4 · outbound

This paper cites an unresolved cited work.

Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 53

Resolution: parse uncertain; raw_fallback, observed 2026-07-09T00:45:49.466888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 735de633-049f-49c4-96c5-4042b80f8874 · outbound

This paper cites Hybrid Least Squares/Gradient Descent Methods for.

Hybrid Least Squares/Gradient Descent Methods for MIONets Hybrid Least Squares/Gradient Descent Methods for

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.381780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 066b2d5b-de98-426f-8c03-0d7643460d11 · outbound

This paper cites A hybrid iterative method based on.

Hybrid Least Squares/Gradient Descent Methods for MIONets A hybrid iterative method based on

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.452280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 261703f8-617a-4825-be27-fda59d6a3967 · outbound

This paper cites Schauder bases in.

Hybrid Least Squares/Gradient Descent Methods for MIONets Schauder bases in

Reference 56

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Hybrid Least Squares/Gradient Descent Methods for MIONets Zur theorie stetiger abbildungen in funktionalr

Reference 57

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Reference 58

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Reference 59

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Hybrid Least Squares/Gradient Descent Methods for MIONets Furuichi, H

Reference 60

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Hybrid Least Squares/Gradient Descent Methods for MIONets The Annals of Statistics , number =

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.389573Z

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Hybrid Least Squares/Gradient Descent Methods for MIONets arXiv preprint math.GM/0508053 , year=

Reference 62

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Reference 63

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Hybrid Least Squares/Gradient Descent Methods for MIONets Advances in Neural Information Processing Systems , volume=

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.413234Z

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Reference 65

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Reference 66

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Hybrid Least Squares/Gradient Descent Methods for MIONets Physical Review Research , volume=

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.378136Z

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Observation a1547d3e-1ec8-4051-9ed7-80b479472d6c · outbound

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Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 68

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Hybrid Least Squares/Gradient Descent Methods for MIONets Unresolved cited work

Reference 69

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Observation 11ecd234-06a7-4066-8734-c1c80d980320 · outbound

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Hybrid Least Squares/Gradient Descent Methods for MIONets , booktitle =

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T00:45:49.374814Z

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Observation 9e8b3549-9265-4be7-bee0-d9eed8bc7e3b · outbound

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Hybrid Least Squares/Gradient Descent Methods for MIONets Tensor Transpose and Its Properties

Reference 71

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Pith citing papers