Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows | Paper Citation Record

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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

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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

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This paper cites Theimpactofsubmesoscalephysicsonprimaryproductivityofplankton.

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Theimpactofsubmesoscalephysicsonprimaryproductivityofplankton

Reference 3

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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This paper cites an unresolved cited work.

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-08T11:17:13.306667Z

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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

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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

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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

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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

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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

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.245645Z

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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

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.227637Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.217816Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.207432Z

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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

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Resolution: verified exact; local_arxiv, observed 2026-08-08T11:17:13.059708Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.197288Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.187334Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.177490Z

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Observation 39c980a7-b675-4847-8302-83f5b77d32c3 · outbound

This paper cites Layer Normalization.

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows Layer Normalization

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.168105Z

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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

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:17:13.157691Z

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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

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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

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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

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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

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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

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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

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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

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