Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data | Paper Citation Record

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This paper cites Scientific multi-agent reinforcement learning for wall-models of turbulent flows.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Scientific multi-agent reinforcement learning for wall-models of turbulent flows

Reference 1

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This paper cites Machine learning for fluid mechanics.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Machine learning for fluid mechanics

Reference 2

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This paper cites Physics-informed neural networks (pinns) for fluid mechanics: A review.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Physics-informed neural networks (pinns) for fluid mechanics: A review

Reference 3

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This paper cites Data-driven discovery of coordinates and governing equations.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Data-driven discovery of coordinates and governing equations

Reference 4

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This paper cites Neural ordinary differential equations.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Neural ordinary differential equations

Reference 5

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This paper cites Lagrangian Neural Networks.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Lagrangian Neural Networks

Reference 6

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This paper cites A numerical study of three-dimensional turbulent channel flow at large reynolds numbers.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data A numerical study of three-dimensional turbulent channel flow at large reynolds numbers

Reference 7

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This paper cites A national strategy for advancing climate modeling.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data A national strategy for advancing climate modeling

Reference 8

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This paper cites Predicting the uncertainty of numerical weather forecasts: A review.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Predicting the uncertainty of numerical weather forecasts: A review

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:32.573378Z

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This paper cites Generative learning for forecasting the dynamics of high-dimensional complex systems.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Generative learning for forecasting the dynamics of high-dimensional complex systems

Reference 10

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This paper cites Hamiltonian neural networks.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Hamiltonian neural networks

Reference 11

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Observation 3a5f1f97-d6ad-4519-a74d-0bf9d16c438f · outbound

This paper cites Generating synthetic data for neural operators.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Generating synthetic data for neural operators

Reference 12

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This paper cites Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

Reference 13

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This paper cites Physics-aware, probabilistic model order reduction with guaranteed stability.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Physics-aware, probabilistic model order reduction with guaranteed stability

Reference 14

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Observation 3d7600d8-5c65-4e49-8689-ca3c8e8621de · outbound

This paper cites Semi-supervised invertible neural operators for bayesian inverse problems.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Semi-supervised invertible neural operators for bayesian inverse problems

Reference 15

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This paper cites Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks

Reference 16

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This paper cites Physics-informed machine learning.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Physics-informed machine learning

Reference 17

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This paper cites Adaptive learning of effective dynamics for online modeling of complex systems.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Adaptive learning of effective dynamics for online modeling of complex systems

Reference 18

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This paper cites Auto-encoding variational bayes, 2013.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Auto-encoding variational bayes, 2013

Reference 19

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This paper cites A library for learning neural operators, 2024.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data A library for learning neural operators, 2024

Reference 20

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This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Neural operator: Learning maps between function spaces with applications to pdes

Reference 21

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

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Fourier Neural Operator for Parametric Partial Differential Equations

Reference 22

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This paper cites On kinematic waves ii.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data On kinematic waves ii

Reference 23

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This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 24

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This paper cites Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Reference 25

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This paper cites Explicit and implicit les closures for burgers turbulence.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Explicit and implicit les closures for burgers turbulence

Reference 26

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This paper cites Automating turbulence modelling by multi-agent reinforcement learning.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Automating turbulence modelling by multi-agent reinforcement learning

Reference 27

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This paper cites Modelling: Build imprecise supercomputers.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Modelling: Build imprecise supercomputers

Reference 28

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This paper cites Numerical approximation of partial differential equations, volume 23.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Numerical approximation of partial differential equations, volume 23

Reference 29

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 30

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This paper cites A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables

Reference 31

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Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Code verification by the method of manufactured solutions

Reference 32

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This paper cites 11 pflop/s simulations of cloud cavitation collapse.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data 11 pflop/s simulations of cloud cavitation collapse

Reference 33

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This paper cites Weak neural variational inference for solving bayesian inverse problems without forward models: applications in elastography.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Weak neural variational inference for solving bayesian inverse problems without forward models: applications in elastography

Reference 34

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This paper cites Proximal Policy Optimization Algorithms.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Proximal Policy Optimization Algorithms

Reference 35

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This paper cites General circulation experiments with the primitive equations: I.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data General circulation experiments with the primitive equations: I

Reference 36

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This paper cites Strategies for turbulence modelling and simulations.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Strategies for turbulence modelling and simulations

Reference 37

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This paper cites Hamiltonian Generative Networks.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Hamiltonian Generative Networks

Reference 38

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This paper cites Multiscale simulations of complex systems by learning their effective dynamics.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Multiscale simulations of complex systems by learning their effective dynamics

Reference 39

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This paper cites Closure discovery for coarse-grained partial differential equations using grid-based reinforcement learning.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Closure discovery for coarse-grained partial differential equations using grid-based reinforcement learning

Reference 40

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Observation 91e02e38-1b26-46e5-adff-c2363ef06801 · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Learning the solution operator of parametric partial differential equations with physics-informed deeponets

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:32.353981Z

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Observation 14ef82b3-48d3-4f4e-a193-0f6d43224ce1 · outbound

This paper cites Tianshou: A highly modularized deep reinforcement learning library.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Tianshou: A highly modularized deep reinforcement learning library

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:32.343752Z

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This paper cites Multiscale model for turbulent flows.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Multiscale model for turbulent flows

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:57:32.333599Z

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Observation c84dcb14-be11-44dd-8d30-bb8f03ebb5eb · outbound

This paper cites Ode2vae: Deep generative second order odes with bayesian neural networks.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Ode2vae: Deep generative second order odes with bayesian neural networks

Reference 44

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This paper cites DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling

Reference 45

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Observation 9fe468ef-528c-4d7c-a5a3-2f9504360364 · outbound

This paper cites Learning Deep CNN Denoiser Prior for Image Restoration.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data Learning Deep CNN Denoiser Prior for Image Restoration

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:57:32.248120Z

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