Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T11:17:12.987437Z
Paper Citation Record · LEDGER
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2502.07990.
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 2026-08-10T06:31:04.303077+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
54 of 54 outbound references displayed
- verified exact2
- verified fuzzy43
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c518d563-7783-4396-8536-5102402b9970 · outbound
This paper cites Multiscale model for turbulent flows.AIAA journal, 26(11):1311–1320, 1988.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Multiscale model for turbulent flows.AIAA journal, 26(11):1311–1320, 1988
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.569632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 11500e17-9273-4171-98bb-aff0fcace95e · outbound
This paper cites Exposure science in the 21st century: a vision and a strategy.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Exposure science in the 21st century: a vision and a strategy
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.558913Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f5c8c839-a9a4-4475-ba43-4ae3ffe50db9 · outbound
This paper cites Theimpactofsubmesoscalephysicsonprimaryproductivityofplankton.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Theimpactofsubmesoscalephysicsonprimaryproductivityofplankton
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.548247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9fcde225-47ab-4b7b-a41a-26ce429a93b3 · outbound
This paper cites Multiscale modeling in biomechanics and mechanobiology.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Multiscale modeling in biomechanics and mechanobiology
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.537822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f04d833e-64cf-414a-b018-c0aa9715ecf6 · outbound
This paper cites Multiscale modeling meets machine learning: What can we learn?Archives of Com- putational Methods in Engineering, 28:1017–1037, 2021.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Multiscale modeling meets machine learning: What can we learn?Archives of Com- putational Methods in Engineering, 28:1017–1037, 2021
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.527943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 92509f09-995b-48fa-8825-c8ede786f37c · outbound
This paper cites Theproperorthogonaldecompositioninthe analysis of turbulent flows.Annual review of fluid mechanics, 25(1):539–575, 1993.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Theproperorthogonaldecompositioninthe analysis of turbulent flows.Annual review of fluid mechanics, 25(1):539–575, 1993
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.518238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2950389e-36b2-477a-b280-6d1b977deb79 · outbound
This paper cites Data-driven operator inference for nonintrusive projection-based model reduction.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Data-driven operator inference for nonintrusive projection-based model reduction
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.506896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 33035afc-9ff1-4aea-8f16-7c74a6cd9ff4 · outbound
This paper cites Nonlinear em- beddings for conserving hamiltonians and other quantities with neural galerkin schemes.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Nonlinear em- beddings for conserving hamiltonians and other quantities with neural galerkin schemes
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.495328Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 53f1f729-14b0-49e5-b5aa-66b9bb58740c · outbound
This paper cites Learning Mesh-Based Simulation with Graph Networks.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Learning Mesh-Based Simulation with Graph Networks
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.838589Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0944b44d-89b7-4d71-80ea-bf9789f9c820 · outbound
This paper cites Physical Design using Differentiable Learned Simulators.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Physical Design using Differentiable Learned Simulators
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.843306Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db01c198-6377-46e2-9d81-2fbfe8098879 · outbound
This paper cites Learning to simulate complex physics with graph networks.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Learning to simulate complex physics with graph networks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.483451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f9c6671c-ff0f-46a8-b8cd-6da455d54b72 · outbound
This paper cites Conditionally parameterized, discretization-awareneuralnetworksformesh-basedmodelingofphysicalsystems.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Conditionally parameterized, discretization-awareneuralnetworksformesh-basedmodelingofphysicalsystems
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.472519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a13f0071-3115-48da-9637-e8dbce89f05a · outbound
This paper cites Graph element networks: adaptive, structured computa- tion and memory.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Graph element networks: adaptive, structured computa- tion and memory
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.462504Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 64c9b280-dcc7-44de-97c2-e4e7a52034f3 · outbound
This paper cites End-to-end differentiable physics for learning and control.Advances in neural information pro- cessing systems, 31, 2018.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows End-to-end differentiable physics for learning and control.Advances in neural information pro- cessing systems, 31, 2018
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.452566Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 43bc684c-4c87-45db-8e59-25d9da8fa4c6 · outbound
This paper cites Graph networks as learnable physics engines for inferenceandcontrol.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Graph networks as learnable physics engines for inferenceandcontrol
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.441682Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fca2f7d4-0804-43e1-b180-9bf5af6d8503 · outbound
This paper cites Modelingthedynamicsofpdesystemswithphysics- constrained deep auto-regressive networks.Journal of Computational Physics, 403:109056, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Modelingthedynamicsofpdesystemswithphysics- constrained deep auto-regressive networks.Journal of Computational Physics, 403:109056, 2020
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.865207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0e2d1ecc-7f25-4171-8d4a-dd421f590cd8 · outbound
This paper cites Learned Coarse Models for Efficient Turbulence Simulation.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Learned Coarse Models for Efficient Turbulence Simulation
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.868369Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4696ed2-2e3e-4834-8964-41beb34153f9 · outbound
This paper cites Professor forcing: A new algorithm for training recurrent net- works.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Professor forcing: A new algorithm for training recurrent net- works
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.423266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b8104b4e-aed8-4e5e-bbe9-52325ccc6b50 · outbound
This paper cites Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.411812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 880f97e5-9788-4a6b-9a18-8ef65f6d24ba · outbound
This paper cites Phycrnet: Physics-informed convolutional-recurrentnetworkforsolvingspatiotemporalpdes.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Phycrnet: Physics-informed convolutional-recurrentnetworkforsolvingspatiotemporalpdes
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.400928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 604eeae5-a0bc-4049-b219-ebe43ad1ba58 · outbound
This paper cites Transformers for modeling physical systems.Neural Networks, 146:272–289, 2022.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Transformers for modeling physical systems.Neural Networks, 146:272–289, 2022
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.881050Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 10d008de-2ea8-4889-bc27-8332e1345c64 · outbound
This paper cites Long short-term memory.Neural computation, 9 (8):1735–1780, 1997.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Long short-term memory.Neural computation, 9 (8):1735–1780, 1997
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.384022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8886678a-5d04-4b3a-b71a-8124836d1be9 · outbound
This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Attention is all you need.Advances in neural information processing systems, 30, 2017
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.886722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d653002e-5f20-4211-a8f2-a000bdae75ad · outbound
This paper cites Deep learning for universal linear em- beddings of nonlinear dynamics.Nature communications, 9(1):1–10, 2018.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Deep learning for universal linear em- beddings of nonlinear dynamics.Nature communications, 9(1):1–10, 2018
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.367856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ed95b1bf-6ccd-4769-8751-d9420a5fe19f · outbound
This paper cites A deeplearningenablerfornonintrusivereducedordermodelingoffluidflows.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows A deeplearningenablerfornonintrusivereducedordermodelingoffluidflows
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.358838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e4f9dbe4-db2e-40e6-a367-63cc97a4696d · outbound
This paper cites Modelreductionofdynamicalsystemsonnonlinearman- ifolds using deep convolutional autoencoders.Journal of Computational Physics, 404:108973, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Modelreductionofdynamicalsystemsonnonlinearman- ifolds using deep convolutional autoencoders.Journal of Computational Physics, 404:108973, 2020
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.349327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d763d7b7-3706-4133-8fc9-6c1529d724b2 · outbound
This paper cites Machine-learning-based reduced-ordermodelingforunsteadyflowsaroundbluffbodiesofvariousshapes.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Machine-learning-based reduced-ordermodelingforunsteadyflowsaroundbluffbodiesofvariousshapes
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.338084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 477832c0-f976-459c-b71e-2dc94c97d058 · outbound
This paper cites Nonlinearmodedecompositionwithconvo- lutional neural networks for fluid dynamics.Journal of Fluid Mechanics, 882, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Nonlinearmodedecompositionwithconvo- lutional neural networks for fluid dynamics.Journal of Fluid Mechanics, 882, 2020
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.327067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 045113bf-9ced-4bfd-ab78-33c512ef7fdc · outbound
This paper cites Recurrentneuralnetworksandkoopman-basedframeworksfortemporalpredictionsinalow- order model of turbulence.International Journal of Heat and Fluid Flow, 90:108816, 2021.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Recurrentneuralnetworksandkoopman-basedframeworksfortemporalpredictionsinalow- order model of turbulence.International Journal of Heat and Fluid Flow, 90:108816, 2021
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.316416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6343647e-6ed9-4f72-bdc5-48787ce29c10 · outbound
This paper cites an unresolved cited work.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Unresolved cited work
Reference 30
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T11:17:13.306667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ac52a15f-1a01-46eb-a0c7-c38eb4c62cef · outbound
This paper cites Convolutional neuralnetworksforfluidflowanalysis: towardeffectivemetamodelingandlowdimensional- ization.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Convolutional neuralnetworksforfluidflowanalysis: towardeffectivemetamodelingandlowdimensional- ization
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.297765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ead0f6e9-0217-42d9-93fd-4b7f96343f89 · outbound
This paper cites Pod-dl-rom: enhancing deep learning-based reduced ordermodelsfornonlinearparametrizedpdesbyproperorthogonaldecomposition.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Pod-dl-rom: enhancing deep learning-based reduced ordermodelsfornonlinearparametrizedpdesbyproperorthogonaldecomposition
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.287897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a4daca48-af10-4e70-890a-a049d7e1b4ce · outbound
This paper cites Mul- tiscale simulations of complex systems by learning their effective dynamics.Nature Machine Intelligence, 4(4):359–366, 2022.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Mul- tiscale simulations of complex systems by learning their effective dynamics.Nature Machine Intelligence, 4(4):359–366, 2022
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.277284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c9efe378-e94a-45e6-a9b8-9ef164f10a48 · outbound
This paper cites Data-driven reduced order model with temporal convolutional neural network.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Data-driven reduced order model with temporal convolutional neural network
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.266195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 05a9d4e1-79da-41fb-bad9-ec1351e229c6 · outbound
This paper cites Deepneu- ral networks for nonlinear model order reduction of unsteady flows.Physics of Fluids, 32(10): 105104, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Deepneu- ral networks for nonlinear model order reduction of unsteady flows.Physics of Fluids, 32(10): 105104, 2020
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.255316Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e16968b6-ada7-4c68-b365-06a4ac0b03bb · outbound
This paper cites Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoen- coders.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoen- coders
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.245645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation caf508bb-202c-4771-9ecd-d40aea79027e · outbound
This paper cites Unsteady reduced-ordermodelofflowovercylindersbasedonconvolutionalanddeconvolutionalneu- ral network structure.Physics of Fluids, 32(12):123609, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Unsteady reduced-ordermodelofflowovercylindersbasedonconvolutionalanddeconvolutionalneu- ral network structure.Physics of Fluids, 32(12):123609, 2020
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.236724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2f5142ad-df27-475a-9832-28465f9b3ff7 · outbound
This paper cites Multi-levelconvolutionalautoencodernetworksforpara- metric prediction of spatio-temporal dynamics.Computer Methods in Applied Mechanics and Engineering, 372:113379, 2020.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Multi-levelconvolutionalautoencodernetworksforpara- metric prediction of spatio-temporal dynamics.Computer Methods in Applied Mechanics and Engineering, 372:113379, 2020
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.227637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 411cd81c-2740-4fbc-82db-79b21b9c29e5 · outbound
This paper cites Eigen-gnn: A graph structure preserving plug-in for gnns.IEEE Transactions on Knowledge and Data Engineering, 35(3):2544– 2555, 2021.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Eigen-gnn: A graph structure preserving plug-in for gnns.IEEE Transactions on Knowledge and Data Engineering, 35(3):2544– 2555, 2021
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.217816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cb6e4022-d95d-42f4-8a24-0f836fc9a9bc · outbound
This paper cites InSC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1058–1070.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows InSC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1058–1070
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.207432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2abd78f4-a9dd-4a29-bb22-f444e516e470 · outbound
This paper cites GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T11:17:13.059708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 96bc246a-235e-4e50-a73a-286f92430908 · outbound
This paper cites Interpretable a-posteriori error indication for graph neural network surrogate models.Computer Methods in Applied Mechanics and Engineer- ing, 433:117509, 2025.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Interpretable a-posteriori error indication for graph neural network surrogate models.Computer Methods in Applied Mechanics and Engineer- ing, 433:117509, 2025
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.197288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 465c37d6-e12f-4ab2-929f-0d08834afbbb · outbound
This paper cites The finite volume method in computational fluid dynamics, fluid mechanics and its applications.Cham: Springer, 113, 2016.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows The finite volume method in computational fluid dynamics, fluid mechanics and its applications.Cham: Springer, 113, 2016
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.187334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 26a879a4-98a6-4f97-972d-3b180360674c · outbound
This paper cites Openfoam: Ac++libraryforcomplex physicssimulations.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Openfoam: Ac++libraryforcomplex physicssimulations
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.177490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 39c980a7-b675-4847-8302-83f5b77d32c3 · outbound
This paper cites Layer Normalization.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Layer Normalization
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.955193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 403bd247-5b68-47a6-87da-5a46616731c8 · outbound
This paper cites Pointnet++: Deephierarchical featurelearningonpointsetsinametricspace.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Pointnet++: Deephierarchical featurelearningonpointsetsinametricspace
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.168105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a0dbece0-89a4-437d-b80e-c82ea2b2beef · outbound
This paper cites Lan- guage models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Lan- guage models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.157691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8df361c1-06e7-4dcd-8d66-01119b84d270 · outbound
This paper cites Multi-fidelity Generative Deep Learning Turbulent Flows.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Multi-fidelity Generative Deep Learning Turbulent Flows
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-08-08T11:17:13.033706Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 80559509-11e0-4051-a21f-4c824ee524b0 · outbound
This paper cites NatureCommunications,15(1):8904,2024.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows NatureCommunications,15(1):8904,2024
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.145353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ed23544c-7a3f-4983-a077-1985d47df223 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Adam: A Method for Stochastic Optimization
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:17:12.972918Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 187f94ab-04af-42ed-9419-66417bcb712a · outbound
This paper cites Neural message passing for quantum chemistry.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Neural message passing for quantum chemistry
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.133417Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation d74f9f21-2849-45ab-9d0c-8210fb4c7f32 · outbound
This paper cites Turbulentflows.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Turbulentflows
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.120982Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 54842450-7701-4cbb-8356-826bf1548c7c · outbound
This paper cites Rethinkingfid: Towardsabetterevaluationmetricforimagegeneration.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Rethinkingfid: Towardsabetterevaluationmetricforimagegeneration
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.111521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 46285fed-0ec6-4f82-8ec3-54e6c8e38545 · outbound
This paper cites Decomposition of the continuous ranked probability score for ensemble pre- diction systems.Weather and Forecasting, 15(5):559–570, 2000.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Decomposition of the continuous ranked probability score for ensemble pre- diction systems.Weather and Forecasting, 15(5):559–570, 2000
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T11:17:13.101421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
No inbound Pith citation observations are available.