Paper Citation Record · LEDGER

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2607.00460.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.00460 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-02T03:48:16.650671Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T18:16:53.317828Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact3
verified fuzzy14
unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b4b0507b-be5c-4dcd-991f-7fab69bf68bc · outbound

This paper cites Super-resolution reconstruction of turbulent flows with machine learning.Journal of Fluid Mechanics, 870:106–120.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Super-resolution reconstruction of turbulent flows with machine learning.Journal of Fluid Mechanics, 870:106–120

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.944634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 281273cb-c585-4679-98d7-aaaf7581381c · outbound

This paper cites Learning mesh-based simulation with graph networks.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Learning mesh-based simulation with graph networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.696456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8c7519e-8a54-4b46-a682-c7898330ce9e · outbound

This paper cites Predicting Physics in Mesh-reduced Space with Temporal Attention.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Predicting Physics in Mesh-reduced Space with Temporal Attention

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:56:34.368796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac3694ff-04a0-42cf-ae1e-476f6fb98d46 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Fourier Neural Operator for Parametric Partial Differential Equations

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-02T03:56:34.363459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fc1fc6b9-0b57-412c-8d70-722476533d24 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.669169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d68e28a7-b98c-4ee0-8c2a-4c631092aa7d · outbound

This paper cites an unresolved cited work.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-07-06T08:42:08.672569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 19c19329-382a-4182-a337-a9377516c4d2 · outbound

This paper cites Surrogate modeling for fluid flows based on physics- constrained deep learning without simulation data.Computer Methods in Applied Mechanics and Engineering, 361:112732, 2020.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Surrogate modeling for fluid flows based on physics- constrained deep learning without simulation data.Computer Methods in Applied Mechanics and Engineering, 361:112732, 2020

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.659553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d079bd5a-7d69-4723-b70f-20de27698488 · outbound

This paper cites Learning data-driven discretizations for partial differential equations.Proceedings of the National Academy of Sciences, 116(31):15344–15349, 2019.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Learning data-driven discretizations for partial differential equations.Proceedings of the National Academy of Sciences, 116(31):15344–15349, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.655486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0f487164-c7d0-4496-a29d-e7467d893205 · outbound

This paper cites Ma- chine learning–accelerated computational fluid dynamics.Proceedings of the National Academy of Sciences, 118(21):e2101784118, 2021.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Ma- chine learning–accelerated computational fluid dynamics.Proceedings of the National Academy of Sciences, 118(21):e2101784118, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.699922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9d33637f-0ef8-490e-97a2-b27e0d50fd73 · outbound

This paper cites Differentiable hybrid neural modeling for fluid-structure interaction.Journal of Computational Physics, 496:112584.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Differentiable hybrid neural modeling for fluid-structure interaction.Journal of Computational Physics, 496:112584

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.693386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5453d123-160b-461b-afd1-722265038a78 · outbound

This paper cites Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers.Advances in neural information processing systems, 33:6111–6122.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers.Advances in neural information processing systems, 33:6111–6122

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.941705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 976c6e3a-9df1-4490-b90b-27f9e2b089a2 · outbound

This paper cites Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics.Communications Physics, 7(1):31, 2024.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics.Communications Physics, 7(1):31, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.947879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc43434c-06ad-48fa-b9b0-da0c8b5be403 · outbound

This paper cites P 2C2Net: PDE-preserved coarse correction network for efficient prediction of spatiotemporal dynamics.Advances in Neural Information Processing Systems, 37:68897–68925, 2024.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction P 2C2Net: PDE-preserved coarse correction network for efficient prediction of spatiotemporal dynamics.Advances in Neural Information Processing Systems, 37:68897–68925, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.676393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df84d94b-28c4-47ba-b846-f60700d504e1 · outbound

This paper cites Learnable-differentiable finite volume solver for accelerated simulation of flows.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Learnable-differentiable finite volume solver for accelerated simulation of flows

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.673588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e602850-26b9-40d1-a855-21daa1109e72 · outbound

This paper cites Data-driven whitney forms for structure-preserving control volume analysis.Journal of Computational Physics, 496:112520.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Data-driven whitney forms for structure-preserving control volume analysis.Journal of Computational Physics, 496:112520

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.687458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0633b927-c1f7-470a-9c50-0919fc63eb51 · outbound

This paper cites Noem: efficient and scalable finite element method enabled by reusable neural operators.Nature Computational Science, 6(4):417–429, 2026.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction Noem: efficient and scalable finite element method enabled by reusable neural operators.Nature Computational Science, 6(4):417–429, 2026

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.649407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5a5b9b45-cd71-4628-9827-303239b99475 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-02T03:56:34.369572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6cf9eac7-eaac-45ff-909a-8eeee3531a84 · outbound

This paper cites JAX: composable transforma- tions of Python+NumPy programs, 2018.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction JAX: composable transforma- tions of Python+NumPy programs, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T08:42:08.937488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9e435a87-5fbe-438d-9958-df88feea3046 · outbound

This paper cites extrapolation.

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction extrapolation

Reference 19

Resolution: malformed identifier; raw_fallback, observed 2026-07-06T08:42:08.690556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation f450adbf-eb53-4ee6-a370-4fb9d2805439 · inbound

Differentiable Hybrid Neural-CFD Modelling of Wall-Bounded Turbulence: Coupled Learning of Subgrid-Scale and Wall Closures cites this paper.

Differentiable Hybrid Neural-CFD Modelling of Wall-Bounded Turbulence: Coupled Learning of Subgrid-Scale and Wall Closures A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

Reference 170

Resolution: unresolved; no resolver link, observed 2026-08-01T18:16:53.317828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.