Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T16:06:43.979284Z
Paper Citation Record · LEDGER
Automated discovery of finite volume schemes using Graph Neural Networks
As of 16 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2508.19052.
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 2026-08-16T06:30:59.297886+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
52 of 52 outbound references displayed
- verified exact0
- verified fuzzy37
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e40d69e6-66a4-4088-ba10-c56498beac8a · outbound
This paper cites an unresolved cited work.
Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work
Reference 1
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 720bd914-b415-48c7-8086-3dc783ee8324 · outbound
This paper cites Relational inductive biases, deep learning, and graph networks.
Automated discovery of finite volume schemes using Graph Neural Networks Relational inductive biases, deep learning, and graph networks
Reference 2
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ff2eb22-9d86-4287-808f-31194eaaf5de · outbound
This paper cites Battaglia, Razvan Pascanu, Matthew Lai, David Rezende, and Koray Kavukcuoglu.
Automated discovery of finite volume schemes using Graph Neural Networks Battaglia, Razvan Pascanu, Matthew Lai, David Rezende, and Koray Kavukcuoglu
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.699402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 37cd42a9-93d0-4565-93e1-b0a83ca96de1 · outbound
This paper cites Accurate medium-range global weather forecasting with 3d neural networks.
Automated discovery of finite volume schemes using Graph Neural Networks Accurate medium-range global weather forecasting with 3d neural networks
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.685061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2ecaf00a-1399-4d7c-882b-c74cb03b208a · outbound
This paper cites Chang, Tomer D.
Automated discovery of finite volume schemes using Graph Neural Networks Chang, Tomer D
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.671189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0e617003-f0d0-4609-8c6f-6e986c85072f · outbound
This paper cites Interpretable machine learning for science with pysr and symbolicregression.jl, 2023.
Automated discovery of finite volume schemes using Graph Neural Networks Interpretable machine learning for science with pysr and symbolicregression.jl, 2023
Reference 6
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e192550-5994-4756-b055-9ce8497de541 · outbound
This paper cites Discovering symbolic models from deep learning with inductive biases.
Automated discovery of finite volume schemes using Graph Neural Networks Discovering symbolic models from deep learning with inductive biases
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.647815Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 151a216e-0748-41b4-946c-a4cf6787d0d7 · outbound
This paper cites Dauphin, Angela Fan, Michael Auli, and David Grangier.
Automated discovery of finite volume schemes using Graph Neural Networks Dauphin, Angela Fan, Michael Auli, and David Grangier
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.633982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cccb9700-92bf-47f9-8082-f2b86d0175ab · outbound
This paper cites Smith, Kelsey R.
Automated discovery of finite volume schemes using Graph Neural Networks Smith, Kelsey R
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.620258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f061c3e3-2bec-4111-a175-6c7bd80e915f · outbound
This paper cites Finite volume informed graph neural network for myocardial perfusion simulation.
Automated discovery of finite volume schemes using Graph Neural Networks Finite volume informed graph neural network for myocardial perfusion simulation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.604956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9442d7e9-fd82-4e12-ac76-8fc4b18f0a36 · outbound
This paper cites Graph neural networks are dynamic programmers.
Automated discovery of finite volume schemes using Graph Neural Networks Graph neural networks are dynamic programmers
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.590329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a9280e9b-d151-4fbf-9f4e-4b3bde7c7040 · outbound
This paper cites Deep fdm: Enhanced finite difference methods by deep learning.
Automated discovery of finite volume schemes using Graph Neural Networks Deep fdm: Enhanced finite difference methods by deep learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.574444Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6d41df88-417a-42fe-864a-def394aaf1e9 · outbound
This paper cites How powerful are k-hop message passing graph neural networks, 2023.
Automated discovery of finite volume schemes using Graph Neural Networks How powerful are k-hop message passing graph neural networks, 2023
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.560813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4a2155db-ab4b-413f-81c2-64371b0cef04 · outbound
This paper cites Meshmask: Physics-based simulations with masked graph neural networks.
Automated discovery of finite volume schemes using Graph Neural Networks Meshmask: Physics-based simulations with masked graph neural networks
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.547017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 88358106-9faa-48c3-81cd-322a95f3a1d6 · outbound
This paper cites Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics.
Automated discovery of finite volume schemes using Graph Neural Networks Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 093d1196-9ce6-4a83-b4c8-e231b929a945 · outbound
This paper cites Gmsh: A 3-d finite element mesh generator with built-in pre- and post-processing facilities.
Automated discovery of finite volume schemes using Graph Neural Networks Gmsh: A 3-d finite element mesh generator with built-in pre- and post-processing facilities
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.532640Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1f32deb5-a932-4fa7-9f0f-5ad59dcad61c · outbound
This paper cites Hachem, B.
Automated discovery of finite volume schemes using Graph Neural Networks Hachem, B
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.516802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 196d12f2-2228-4b41-bb23-832c9863baf5 · outbound
This paper cites Gaussian error linear units (gelus), 2023.
Automated discovery of finite volume schemes using Graph Neural Networks Gaussian error linear units (gelus), 2023
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4d245e6e-7821-49d2-990d-1e288418dadc · outbound
This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.
Automated discovery of finite volume schemes using Graph Neural Networks Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang
Reference 19
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a8ba3c58-e0c2-4556-ab54-13c1b72d84d3 · outbound
This paper cites Kingma and Jimmy Ba.
Automated discovery of finite volume schemes using Graph Neural Networks Kingma and Jimmy Ba
Reference 20
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2b944162-6908-447c-b81e-452d6b07bcea · outbound
This paper cites Neural relational inference for interacting systems.
Automated discovery of finite volume schemes using Graph Neural Networks Neural relational inference for interacting systems
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.476300Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 54ccc7af-2716-4772-a13e-65cf88f79b5c · outbound
This paper cites Stuart, and Anima Anandkumar.
Automated discovery of finite volume schemes using Graph Neural Networks Stuart, and Anima Anandkumar
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.462766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation edb19641-7d4a-413d-9211-c7a4d7b2b23d · outbound
This paper cites Investigating the ability of pinns to solve burgers’ pde near finite-time blowup, 2024.
Automated discovery of finite volume schemes using Graph Neural Networks Investigating the ability of pinns to solve burgers’ pde near finite-time blowup, 2024
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.449351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0a1dd8f5-c49d-44f0-a07e-f725ed7f10ae · outbound
This paper cites Graphcast: Learning skillful medium-range global weather forecasting.
Automated discovery of finite volume schemes using Graph Neural Networks Graphcast: Learning skillful medium-range global weather forecasting
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.435784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c040507a-1eb8-4b5b-97b7-67255041b759 · outbound
This paper cites Physics- informed machine learning for enhanced 4D flow.
Automated discovery of finite volume schemes using Graph Neural Networks Physics- informed machine learning for enhanced 4D flow
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.421705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5e00bde3-3f57-48dd-a2c2-abe3d2fa714a · outbound
This paper cites Rediscovering orbital mechanics with machine learning.Monthly Notices of the Royal Astronomical Society, 514(3):3965–3977, 2022.
Automated discovery of finite volume schemes using Graph Neural Networks Rediscovering orbital mechanics with machine learning.Monthly Notices of the Royal Astronomical Society, 514(3):3965–3977, 2022
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.408325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2ef7281a-95d6-4769-adbe-a5ac247c4deb · outbound
This paper cites Finite volume graph network (fvgn): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network.
Automated discovery of finite volume schemes using Graph Neural Networks Finite volume graph network (fvgn): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.394155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bb24acf8-1b9d-454e-b278-af644c908cc0 · outbound
This paper cites Learning to solve pdes with finite volume-informed neural networks in a data-free approach.
Automated discovery of finite volume schemes using Graph Neural Networks Learning to solve pdes with finite volume-informed neural networks in a data-free approach
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.380747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6dbaa18c-cdfe-4079-b9a3-e08ddd98e845 · outbound
This paper cites Fourier neural operator for parametric partial differential equations.
Automated discovery of finite volume schemes using Graph Neural Networks Fourier neural operator for parametric partial differential equations
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.366726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 880dd86f-705f-4ba1-adf1-1a2a48e6e8d6 · outbound
This paper cites Multipole graph neural operator for parametric partial differential equations.
Automated discovery of finite volume schemes using Graph Neural Networks Multipole graph neural operator for parametric partial differential equations
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.352830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0c4528d8-6f99-45b4-8d8f-f245b7c25021 · outbound
This paper cites Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.
Automated discovery of finite volume schemes using Graph Neural Networks Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.338338Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 94528f64-1edf-42e7-bb7c-0003ee4cbbb1 · outbound
This paper cites Tenenbaum, and Daniel L.
Automated discovery of finite volume schemes using Graph Neural Networks Tenenbaum, and Daniel L
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.322306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 76c7804b-ca17-4165-8c37-31ff0e85277c · outbound
This paper cites Graph neural networks extrapolate out-of-distribution for shortest paths.
Automated discovery of finite volume schemes using Graph Neural Networks Graph neural networks extrapolate out-of-distribution for shortest paths
Reference 33
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41ddf4b6-91fb-49df-beac-762fe918c3d4 · outbound
This paper cites Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators.
Automated discovery of finite volume schemes using Graph Neural Networks Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.308466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 64564796-6c4b-4e8d-8764-a857f722b4e6 · outbound
This paper cites Learning mesh-based simulation with graph networks.
Automated discovery of finite volume schemes using Graph Neural Networks Learning mesh-based simulation with graph networks
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.293950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b8cce18a-dbd9-42b2-b096-70561847d83b · outbound
This paper cites Karniadakis.
Automated discovery of finite volume schemes using Graph Neural Networks Karniadakis
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.279737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 55512e81-a81c-498a-b886-a5d87c5a4118 · outbound
This paper cites Learning to simulate complex physics with graph networks.
Automated discovery of finite volume schemes using Graph Neural Networks Learning to simulate complex physics with graph networks
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.265511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cb6379b3-e251-45f1-bc70-a9468acd72bc · outbound
This paper cites Graph networks as learnable physics engines for inference and control.
Automated discovery of finite volume schemes using Graph Neural Networks Graph networks as learnable physics engines for inference and control
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:06:43.918459Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31923e6e-2d60-4f4f-8d6f-e424a1817f6f · outbound
This paper cites Distilling free-form natural laws from experimental data.
Automated discovery of finite volume schemes using Graph Neural Networks Distilling free-form natural laws from experimental data
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:06:43.922863Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b86478dd-b9d0-448c-bfff-a4805895116e · outbound
This paper cites Ai feynman: A physics-inspired method for symbolic regression.
Automated discovery of finite volume schemes using Graph Neural Networks Ai feynman: A physics-inspired method for symbolic regression
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:06:43.927006Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a64addd-0102-495a-8f05-a4aa0f28e336 · outbound
This paper cites Chang, Ashesh Rambachan, and Sendhil Mullainathan.
Automated discovery of finite volume schemes using Graph Neural Networks Chang, Ashesh Rambachan, and Sendhil Mullainathan
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.232903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1daa1980-61e1-4c1c-a63c-df1a0975ad0f · outbound
This paper cites Neural algorithmic reasoning.
Automated discovery of finite volume schemes using Graph Neural Networks Neural algorithmic reasoning
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.218910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 964e4fc7-13e9-4b5e-a86c-d1ce017c0586 · outbound
This paper cites Neural execution of graph algorithms.
Automated discovery of finite volume schemes using Graph Neural Networks Neural execution of graph algorithms
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.205637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 68f68169-7184-4b0d-8e2a-cc64363269b2 · outbound
This paper cites an unresolved cited work.
Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work
Reference 44
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T16:06:44.191574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c11cf5be-a101-4e87-81f3-d524468d7cb6 · outbound
This paper cites Asymptotic self-similar blow-up profile for three-dimensional axisymmetric euler equations using neural networks, 2023.
Automated discovery of finite volume schemes using Graph Neural Networks Asymptotic self-similar blow-up profile for three-dimensional axisymmetric euler equations using neural networks, 2023
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.177838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 140cec9a-41e2-4fc9-b7ca-b258cd6dfe91 · outbound
This paper cites an unresolved cited work.
Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work
Reference 46
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T16:06:44.163857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 93b4dbcd-b520-4bf1-9125-b007d29a7b0b · outbound
This paper cites an unresolved cited work.
Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work
Reference 47
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T16:06:44.148505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 196c5f0d-7a4a-4742-9030-9bf0c427882c · outbound
This paper cites Finally, we use those inequalities to bound the error on a general graph.
Automated discovery of finite volume schemes using Graph Neural Networks Finally, we use those inequalities to bound the error on a general graph
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.132883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fe62a2a3-257a-48a6-8575-6f1193b8ff0e · outbound
This paper cites an unresolved cited work.
Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work
Reference 49
- Resolution
- unresolved
- raw_fallback, observed 2026-08-05T16:06:44.118536Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d99becac-a64d-460a-855b-393655d2ce5b · outbound
This paper cites This leads to Theorem C.5 where we demonstrate that a small training error requires at least 2mL` 2m` 4 parameters.
Automated discovery of finite volume schemes using Graph Neural Networks This leads to Theorem C.5 where we demonstrate that a small training error requires at least 2mL` 2m` 4 parameters
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.104016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3cfa753a-5284-48b1-9cc8-db2ec909e9b4 · outbound
This paper cites C.1 Sparsity consideration Lemma C.1.
Automated discovery of finite volume schemes using Graph Neural Networks C.1 Sparsity consideration Lemma C.1
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.088630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 56a73736-d43e-48dd-9f6c-f53f57e38c90 · outbound
This paper cites W a p “ » ———– 1 0 0.
Automated discovery of finite volume schemes using Graph Neural Networks W a p “ » ———– 1 0 0
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T16:06:44.073699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.