Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:49:14.168409Z
Paper Citation Record · LEDGER
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions
As of 19 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2412.07541.
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.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:12:03.610458Z
A source-named dated measurement, never combined with another source.
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Reference resolution
62 of 62 outbound references displayed
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External citation measurements
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Outbound references
Observation 1c6290bb-3e29-4f91-b48d-3b94329b7402 · outbound
This paper cites write newline.
Reference 1
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 2
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
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Reference 5
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions G., Pearlmutter, B
Reference 6
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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al
Reference 7
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils
Reference 9
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Observation 6cb01032-82fa-47d7-a611-e51dffb749e0 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Adam: A Method for Stochastic Optimization
Reference 11
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions S., and Ray, D
Reference 13
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions A Tutorial on Bayesian Optimization
Reference 15
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions and Raviart, P.-A
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions and Houston, P
Reference 20
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Reference 22
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
Reference 23
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions A., Alieva, A., Wang, Q., Brenner, M
Reference 24
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions E., Likas, A., and Fotiadis, D
Reference 26
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Fourier Neural Operator for Parametric Partial Differential Equations
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions and Wendroff, B
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions G., Manickam, I., Trask, N
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Observation 9eeacc50-b6c8-4065-8640-be5b01af7834 · outbound
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Learned Coarse Models for Efficient Turbulence Simulation
Reference 49
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Observation ffbf08ca-a0f5-4ea8-bebd-c3e89cc17ffb · outbound
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A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions FiniteNet: A Fully Convolutional LSTM Network Architecture for Time-Dependent Partial Differential Equations
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 66c7654c-38c9-42f4-b753-b74c09954e83 · outbound
This paper cites an unresolved cited work.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 54
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T18:49:14.394647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bc06cfec-f321-46b9-bd80-69b4d8bff3a6 · outbound
This paper cites Factorized Fourier Neural Operators.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Factorized Fourier Neural Operators
Reference 55
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb317288-5e47-4100-8ef8-414bad6ae9d0 · outbound
This paper cites D., Van Leer, B., and Roberts Jr, W.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions D., Van Leer, B., and Roberts Jr, W
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T18:49:14.383526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 50b76193-9d11-4967-a66e-e4c8681e0c32 · outbound
This paper cites an unresolved cited work.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 57
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T18:49:14.370061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 12f16c64-18db-4eda-aa0f-c4559f1af320 · outbound
This paper cites an unresolved cited work.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 58
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T18:49:14.358626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 15f7aca8-034b-4d7f-bc94-093cc09f574b · outbound
This paper cites and Colella, P.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions and Colella, P
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T18:49:14.347959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 24babb4d-f462-4182-b61a-bbdf201d2f66 · outbound
This paper cites an unresolved cited work.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 60
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T18:49:14.337617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f7d40079-eb6b-4b1d-aa9a-e1885264b6b4 · outbound
This paper cites an unresolved cited work.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions Unresolved cited work
Reference 61
- Resolution
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- raw_fallback, observed 2026-08-11T18:49:14.325956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 04cdacdd-4b10-487e-bbea-4ad367a426b4 · outbound
This paper cites P., and Hoyer, S.
A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions P., and Hoyer, S
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T18:49:14.314268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation 83689fbe-be41-46f9-a994-6135ce5253f0 · inbound
Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations cites this paper.
Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computations A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:12:03.610458Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1388ef85-98e6-448b-b331-858cf3234a25 · inbound
An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study cites this paper.
An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study A data-driven learned discretization approach in finite volume schemes for hyperbolic conservation laws and varying boundary conditions
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T17:54:57.822166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.