Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:33:26.508062Z
Paper Citation Record · LEDGER
Can neural operators always be continuously discretized?
As of 14 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2412.03393.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
57 of 57 outbound references displayed
- verified exact7
- verified fuzzy25
- unresolved24
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7d90122f-aeab-413c-938d-28332f411f33 · outbound
This paper cites Neural operator: Learning maps between function spaces with applications to pdes.
Can neural operators always be continuously discretized? Neural operator: Learning maps between function spaces with applications to pdes
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:28.142237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 68d06a8d-dfdd-4d81-882f-6ce54a28b251 · outbound
This paper cites Deep learning methods for flood mapping: a review of existing applications and future research directions.
Can neural operators always be continuously discretized? Deep learning methods for flood mapping: a review of existing applications and future research directions
Reference 2
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a7dbfd1c-fb13-4f6a-9f5b-7f3ed68e6142 · outbound
This paper cites Physics-informed deep neural operator networks.
Can neural operators always be continuously discretized? Physics-informed deep neural operator networks
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1ec297b8-7246-492f-ab7f-00ca93a2f4aa · outbound
This paper cites Fourier neural operator with learned deformations for pdes on general geometries.
Can neural operators always be continuously discretized? Fourier neural operator with learned deformations for pdes on general geometries
Reference 4
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f8bdbb7-bc20-4112-809b-34cd76ea5860 · outbound
This paper cites Scientific discovery in the age of artificial intelligence.
Can neural operators always be continuously discretized? Scientific discovery in the age of artificial intelligence
Reference 5
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54025cf5-5c11-4448-8e9d-5c5da083d366 · outbound
This paper cites Integrating scientific knowledge with machine learning for engineering and environmental systems.
Can neural operators always be continuously discretized? Integrating scientific knowledge with machine learning for engineering and environmental systems
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.943355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7edb6dba-994a-45ce-bc2f-8b9611b65c09 · outbound
This paper cites The reversible residual network: Backpropagation without storing activations.
Can neural operators always be continuously discretized? The reversible residual network: Backpropagation without storing activations
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.931037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1a409fad-716a-438f-b9d2-950027474721 · outbound
This paper cites Universal approximation property of invertible neural networks.
Can neural operators always be continuously discretized? Universal approximation property of invertible neural networks
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.920016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b02a8cde-51ca-4916-b6ed-de1028060605 · outbound
This paper cites Non-euclidean universal approximation.
Can neural operators always be continuously discretized? Non-euclidean universal approximation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.909254Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 162020ae-0bfa-4a41-9282-72a38c05fac9 · outbound
This paper cites Globally injective relu networks.
Can neural operators always be continuously discretized? Globally injective relu networks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.898347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation d8524fcd-ede4-4290-899a-c7b32b428267 · outbound
This paper cites Coupling-based invertible neural networks are universal diffeomorphism approximators.
Can neural operators always be continuously discretized? Coupling-based invertible neural networks are universal diffeomorphism approximators
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.887261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 587306eb-cfa4-4b84-90b3-87235dda5411 · outbound
This paper cites Image style transfer using convolutional neural networks.
Can neural operators always be continuously discretized? Image style transfer using convolutional neural networks
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.876519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 2befd339-0fff-4b90-9035-bf81097080dc · outbound
This paper cites Diffusion generative models in infinite dimensions.
Can neural operators always be continuously discretized? Diffusion generative models in infinite dimensions
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.866440Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 233efb30-56a3-471b-aa39-95fd309dfeb9 · outbound
This paper cites Error estimates for deeponets: A deep learning framework in infinite dimensions.
Can neural operators always be continuously discretized? Error estimates for deeponets: A deep learning framework in infinite dimensions
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.811276Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 701d97e5-7d99-40e6-b6e3-33dd34f4c1cd · outbound
This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.
Can neural operators always be continuously discretized? DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f2b5bb1-4c2f-4ec3-b22a-c11c1a293d30 · outbound
This paper cites Model reduction and neural networks for parametric pdes.
Can neural operators always be continuously discretized? Model reduction and neural networks for parametric pdes
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.718352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bc3f3879-d205-4d84-9061-4f1b0f501bb9 · outbound
This paper cites The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks.
Can neural operators always be continuously discretized? The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks
Reference 17
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16136c73-5fca-4497-8859-25a59cf68d0a · outbound
This paper cites Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces.
Can neural operators always be continuously discretized? Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-11T22:33:27.083729Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 29c4a682-ad46-4907-844d-606a30e0115b · outbound
This paper cites Globally injective and bijective neural operators.
Can neural operators always be continuously discretized? Globally injective and bijective neural operators
Reference 19
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f0914a7-f387-4e1b-adb7-262bfd878bcb · outbound
This paper cites Discretization error of fourier neural operators.
Can neural operators always be continuously discretized? Discretization error of fourier neural operators
Reference 20
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d493b81-20b6-41ff-be81-ca3c1692f0d8 · outbound
This paper cites Mathematical foundations of infinite-dimensional statistical models.
Can neural operators always be continuously discretized? Mathematical foundations of infinite-dimensional statistical models
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.555365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e36e562a-8b6f-4593-a08a-fc93e40b4cdb · outbound
This paper cites a chter, Dmytro Perekrestenko, Philipp Grohs, and Helmut B \.
Can neural operators always be continuously discretized? a chter, Dmytro Perekrestenko, Philipp Grohs, and Helmut B \
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.493478Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation d6d37f2f-fe0e-4412-8d49-fa3ceec62a2f · outbound
This paper cites Position: Categorical Deep Learning is an Algebraic Theory of All Architectures.
Can neural operators always be continuously discretized? Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Reference 23
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cbe41b55-2e14-4e91-b66f-b5d741809a5e · outbound
This paper cites On locking and robustness in the finite element method.
Can neural operators always be continuously discretized? On locking and robustness in the finite element method
Reference 24
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.874578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1e00cb27-97cf-4230-a596-7b39e8573b76 · outbound
This paper cites Locking effects in the finite element approximation of plate models.
Can neural operators always be continuously discretized? Locking effects in the finite element approximation of plate models
Reference 25
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.863461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 531e7d67-ddd1-4591-b41b-b6fb00cd7189 · outbound
This paper cites Statistical and computational inverse problems, volume 160 of Applied Mathematical Sciences.
Can neural operators always be continuously discretized? Statistical and computational inverse problems, volume 160 of Applied Mathematical Sciences
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.483166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4ac39d44-ab68-49c8-a961-e855740dc471 · outbound
This paper cites Can one use total variation prior for edge-preserving bayesian inversion? Inverse problems, 20 0 (5): 0 1537, 2004.
Can neural operators always be continuously discretized? Can one use total variation prior for edge-preserving bayesian inversion? Inverse problems, 20 0 (5): 0 1537, 2004
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.473006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 86d866a7-051f-4465-9183-6931c4bb9895 · outbound
This paper cites Inverse problems: a bayesian perspective.
Can neural operators always be continuously discretized? Inverse problems: a bayesian perspective
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.462218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 697e7aa7-0f08-41b7-a564-55a4b2cac388 · outbound
This paper cites Discretization-invariant Bayesian inversion and Besov space priors.
Can neural operators always be continuously discretized? Discretization-invariant Bayesian inversion and Besov space priors
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.675366Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c863c5c3-80f2-418c-95be-34088276108a · outbound
This paper cites Besov priors for B ayesian inverse problems.
Can neural operators always be continuously discretized? Besov priors for B ayesian inverse problems
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.685598Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1311135a-b4c1-4282-a1ec-0cab13d250bc · outbound
This paper cites Linear inverse problems for generalised random variables.
Can neural operators always be continuously discretized? Linear inverse problems for generalised random variables
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.451622Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation f70698f2-d325-4819-b2ec-7a961d357682 · outbound
This paper cites Bauschke and Patrick L.
Can neural operators always be continuously discretized? Bauschke and Patrick L
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.807880Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation df9c8ac6-8973-4477-aaec-12ebf04fdbe6 · outbound
This paper cites Nonlocality and Nonlinearity Implies Universality in Operator Learning.
Can neural operators always be continuously discretized? Nonlocality and Nonlinearity Implies Universality in Operator Learning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.819899Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 857376d1-1642-4b65-96eb-0132c655d658 · outbound
This paper cites The homotopy type of the unitary group of hilbert space.
Can neural operators always be continuously discretized? The homotopy type of the unitary group of hilbert space
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.440620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 51b04d5a-8804-4c4c-9e1d-99c15ef5ca42 · outbound
This paper cites Putnam and Aurel Wintner.
Can neural operators always be continuously discretized? Putnam and Aurel Wintner
Reference 35
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.838257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ea862c11-6163-40fa-a8c6-37641f0791e5 · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.938351Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b9bee99-ad3b-4d2b-be1e-bf0f3c58786c · outbound
This paper cites Tyrrell Rockafellar and Roger J.-B.
Can neural operators always be continuously discretized? Tyrrell Rockafellar and Roger J.-B
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:25.945822Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b05f7cf4-70aa-4b2b-8f9e-d31684361c92 · outbound
This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.
Can neural operators always be continuously discretized? Fourier Neural Operator for Parametric Partial Differential Equations
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.000990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02618044-75ac-42f6-ba9e-71169e1d1037 · outbound
This paper cites Sorting out lipschitz function approximation.
Can neural operators always be continuously discretized? Sorting out lipschitz function approximation
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.419147Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e920a798-27eb-4d60-90fd-25c84f20faf6 · outbound
This paper cites Error bounds for approximations with deep relu networks.
Can neural operators always be continuously discretized? Error bounds for approximations with deep relu networks
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.075272Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a901d745-7ee3-41e5-ab46-ee3d58feab08 · outbound
This paper cites Error bounds for approximations with deep R e LU neural networks in W^ s,p norms.
Can neural operators always be continuously discretized? Error bounds for approximations with deep R e LU neural networks in W^ s,p norms
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.087336Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ba5ddc0-6351-4919-94ce-3a1a70b920c7 · outbound
This paper cites A nonlinear discretization theory.
Can neural operators always be continuously discretized? A nonlinear discretization theory
Reference 42
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.798866Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 19982a57-69f2-437d-8300-6a5915f0cf8d · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.179013Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f517838d-9d79-4081-b509-b36c60eec557 · outbound
This paper cites Trudinger.
Can neural operators always be continuously discretized? Trudinger
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.389108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 209360a4-7a8d-4d12-bd0c-e699bf71892c · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 45
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.729121Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e92d9973-2b2a-4e22-ae53-f7d29298f667 · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 46
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.689690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation de830a73-0439-4ba3-8657-aa36c07bd76f · outbound
This paper cites Numerical methods for nonlinear elliptic differential equations.
Can neural operators always be continuously discretized? Numerical methods for nonlinear elliptic differential equations
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.347796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e56b942-d17e-4838-9231-19228e9ab984 · outbound
This paper cites Putnam and Aurel Wintner.
Can neural operators always be continuously discretized? Putnam and Aurel Wintner
Reference 48
- Resolution
- verified exact
- doi, observed 2026-08-11T22:33:26.667461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 321f8482-96b6-4f47-b37f-f988dfd6a59d · outbound
This paper cites Topological degree theory and applications, volume 10 of Series in Mathematical Analysis and Applications.
Can neural operators always be continuously discretized? Topological degree theory and applications, volume 10 of Series in Mathematical Analysis and Applications
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.377983Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92cc485b-4b04-45d0-a8b3-ac0aae27102a · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 50
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T22:33:27.339892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation afc74a02-75c4-4cee-83a9-e04eef6c7872 · outbound
This paper cites an unresolved cited work.
Can neural operators always be continuously discretized? Unresolved cited work
Reference 51
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T22:33:27.304348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 5386c1c5-a6d0-4f85-9386-22fca4a5778d · outbound
This paper cites Set theory.
Can neural operators always be continuously discretized? Set theory
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.393658Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 365e9f2c-c750-4c07-a575-95fde0d07f06 · outbound
This paper cites Michor and David Mumford.
Can neural operators always be continuously discretized? Michor and David Mumford
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.398383Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9198e1ef-8e8d-45a8-9d95-0051ef6ca939 · outbound
This paper cites Martin and Patrizio Neff.
Can neural operators always be continuously discretized? Martin and Patrizio Neff
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.208104Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 02268b97-bf61-4f33-9a41-543901c14f2c · outbound
This paper cites Geometric methods and applications, volume 38 of Texts in Applied Mathematics.
Can neural operators always be continuously discretized? Geometric methods and applications, volume 38 of Texts in Applied Mathematics
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:33:26.420738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73a1fd72-7095-4fbc-8c95-7548871b13b5 · outbound
This paper cites Error bounds for approximations with deep relu neural networks in w s, p norms.
Can neural operators always be continuously discretized? Error bounds for approximations with deep relu neural networks in w s, p norms
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T22:33:27.150817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6638b033-e404-4f3a-a2ad-6b5101bb9d1c · outbound
This paper cites Approximations with deep neural networks in sobolev time-space.
Can neural operators always be continuously discretized? Approximations with deep neural networks in sobolev time-space
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