Automated discovery of finite volume schemes using Graph Neural Networks | Paper Citation Record

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Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work

Reference 1

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This paper cites Relational inductive biases, deep learning, and graph networks.

Automated discovery of finite volume schemes using Graph Neural Networks Relational inductive biases, deep learning, and graph networks

Reference 2

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This paper cites Battaglia, Razvan Pascanu, Matthew Lai, David Rezende, and Koray Kavukcuoglu.

Automated discovery of finite volume schemes using Graph Neural Networks Battaglia, Razvan Pascanu, Matthew Lai, David Rezende, and Koray Kavukcuoglu

Reference 3

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This paper cites Accurate medium-range global weather forecasting with 3d neural networks.

Automated discovery of finite volume schemes using Graph Neural Networks Accurate medium-range global weather forecasting with 3d neural networks

Reference 4

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Observation 2ecaf00a-1399-4d7c-882b-c74cb03b208a · outbound

This paper cites Chang, Tomer D.

Automated discovery of finite volume schemes using Graph Neural Networks Chang, Tomer D

Reference 5

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Observation 0e617003-f0d0-4609-8c6f-6e986c85072f · outbound

This paper cites Interpretable machine learning for science with pysr and symbolicregression.jl, 2023.

Automated discovery of finite volume schemes using Graph Neural Networks Interpretable machine learning for science with pysr and symbolicregression.jl, 2023

Reference 6

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This paper cites Discovering symbolic models from deep learning with inductive biases.

Automated discovery of finite volume schemes using Graph Neural Networks Discovering symbolic models from deep learning with inductive biases

Reference 7

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This paper cites Dauphin, Angela Fan, Michael Auli, and David Grangier.

Automated discovery of finite volume schemes using Graph Neural Networks Dauphin, Angela Fan, Michael Auli, and David Grangier

Reference 8

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Observation cccb9700-92bf-47f9-8082-f2b86d0175ab · outbound

This paper cites Smith, Kelsey R.

Automated discovery of finite volume schemes using Graph Neural Networks Smith, Kelsey R

Reference 9

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Observation f061c3e3-2bec-4111-a175-6c7bd80e915f · outbound

This paper cites Finite volume informed graph neural network for myocardial perfusion simulation.

Automated discovery of finite volume schemes using Graph Neural Networks Finite volume informed graph neural network for myocardial perfusion simulation

Reference 10

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This paper cites Graph neural networks are dynamic programmers.

Automated discovery of finite volume schemes using Graph Neural Networks Graph neural networks are dynamic programmers

Reference 11

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Observation a9280e9b-d151-4fbf-9f4e-4b3bde7c7040 · outbound

This paper cites Deep fdm: Enhanced finite difference methods by deep learning.

Automated discovery of finite volume schemes using Graph Neural Networks Deep fdm: Enhanced finite difference methods by deep learning

Reference 12

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Observation 6d41df88-417a-42fe-864a-def394aaf1e9 · outbound

This paper cites How powerful are k-hop message passing graph neural networks, 2023.

Automated discovery of finite volume schemes using Graph Neural Networks How powerful are k-hop message passing graph neural networks, 2023

Reference 13

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Observation 4a2155db-ab4b-413f-81c2-64371b0cef04 · outbound

This paper cites Meshmask: Physics-based simulations with masked graph neural networks.

Automated discovery of finite volume schemes using Graph Neural Networks Meshmask: Physics-based simulations with masked graph neural networks

Reference 14

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This paper cites Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics.

Automated discovery of finite volume schemes using Graph Neural Networks Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics

Reference 15

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This paper cites Gmsh: A 3-d finite element mesh generator with built-in pre- and post-processing facilities.

Automated discovery of finite volume schemes using Graph Neural Networks Gmsh: A 3-d finite element mesh generator with built-in pre- and post-processing facilities

Reference 16

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Observation 1f32deb5-a932-4fa7-9f0f-5ad59dcad61c · outbound

This paper cites Hachem, B.

Automated discovery of finite volume schemes using Graph Neural Networks Hachem, B

Reference 17

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This paper cites Gaussian error linear units (gelus), 2023.

Automated discovery of finite volume schemes using Graph Neural Networks Gaussian error linear units (gelus), 2023

Reference 18

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This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Automated discovery of finite volume schemes using Graph Neural Networks Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 19

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This paper cites Kingma and Jimmy Ba.

Automated discovery of finite volume schemes using Graph Neural Networks Kingma and Jimmy Ba

Reference 20

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This paper cites Neural relational inference for interacting systems.

Automated discovery of finite volume schemes using Graph Neural Networks Neural relational inference for interacting systems

Reference 21

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This paper cites Stuart, and Anima Anandkumar.

Automated discovery of finite volume schemes using Graph Neural Networks Stuart, and Anima Anandkumar

Reference 22

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This paper cites Investigating the ability of pinns to solve burgers’ pde near finite-time blowup, 2024.

Automated discovery of finite volume schemes using Graph Neural Networks Investigating the ability of pinns to solve burgers’ pde near finite-time blowup, 2024

Reference 23

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This paper cites Graphcast: Learning skillful medium-range global weather forecasting.

Automated discovery of finite volume schemes using Graph Neural Networks Graphcast: Learning skillful medium-range global weather forecasting

Reference 24

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Observation c040507a-1eb8-4b5b-97b7-67255041b759 · outbound

This paper cites Physics- informed machine learning for enhanced 4D flow.

Automated discovery of finite volume schemes using Graph Neural Networks Physics- informed machine learning for enhanced 4D flow

Reference 25

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This paper cites Rediscovering orbital mechanics with machine learning.Monthly Notices of the Royal Astronomical Society, 514(3):3965–3977, 2022.

Automated discovery of finite volume schemes using Graph Neural Networks Rediscovering orbital mechanics with machine learning.Monthly Notices of the Royal Astronomical Society, 514(3):3965–3977, 2022

Reference 26

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This paper cites Finite volume graph network (fvgn): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network.

Automated discovery of finite volume schemes using Graph Neural Networks Finite volume graph network (fvgn): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network

Reference 27

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Observation bb24acf8-1b9d-454e-b278-af644c908cc0 · outbound

This paper cites Learning to solve pdes with finite volume-informed neural networks in a data-free approach.

Automated discovery of finite volume schemes using Graph Neural Networks Learning to solve pdes with finite volume-informed neural networks in a data-free approach

Reference 28

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This paper cites Fourier neural operator for parametric partial differential equations.

Automated discovery of finite volume schemes using Graph Neural Networks Fourier neural operator for parametric partial differential equations

Reference 29

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This paper cites Multipole graph neural operator for parametric partial differential equations.

Automated discovery of finite volume schemes using Graph Neural Networks Multipole graph neural operator for parametric partial differential equations

Reference 30

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This paper cites Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Automated discovery of finite volume schemes using Graph Neural Networks Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 31

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This paper cites Tenenbaum, and Daniel L.

Automated discovery of finite volume schemes using Graph Neural Networks Tenenbaum, and Daniel L

Reference 32

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This paper cites Graph neural networks extrapolate out-of-distribution for shortest paths.

Automated discovery of finite volume schemes using Graph Neural Networks Graph neural networks extrapolate out-of-distribution for shortest paths

Reference 33

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Automated discovery of finite volume schemes using Graph Neural Networks Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.308466Z

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Observation 64564796-6c4b-4e8d-8764-a857f722b4e6 · outbound

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

Automated discovery of finite volume schemes using Graph Neural Networks Learning mesh-based simulation with graph networks

Reference 35

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This paper cites Karniadakis.

Automated discovery of finite volume schemes using Graph Neural Networks Karniadakis

Reference 36

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Observation 55512e81-a81c-498a-b886-a5d87c5a4118 · outbound

This paper cites Learning to simulate complex physics with graph networks.

Automated discovery of finite volume schemes using Graph Neural Networks Learning to simulate complex physics with graph networks

Reference 37

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Observation cb6379b3-e251-45f1-bc70-a9468acd72bc · outbound

This paper cites Graph networks as learnable physics engines for inference and control.

Automated discovery of finite volume schemes using Graph Neural Networks Graph networks as learnable physics engines for inference and control

Reference 38

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Automated discovery of finite volume schemes using Graph Neural Networks Distilling free-form natural laws from experimental data

Reference 39

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This paper cites Ai feynman: A physics-inspired method for symbolic regression.

Automated discovery of finite volume schemes using Graph Neural Networks Ai feynman: A physics-inspired method for symbolic regression

Reference 40

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This paper cites Chang, Ashesh Rambachan, and Sendhil Mullainathan.

Automated discovery of finite volume schemes using Graph Neural Networks Chang, Ashesh Rambachan, and Sendhil Mullainathan

Reference 41

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This paper cites Neural algorithmic reasoning.

Automated discovery of finite volume schemes using Graph Neural Networks Neural algorithmic reasoning

Reference 42

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Observation 964e4fc7-13e9-4b5e-a86c-d1ce017c0586 · outbound

This paper cites Neural execution of graph algorithms.

Automated discovery of finite volume schemes using Graph Neural Networks Neural execution of graph algorithms

Reference 43

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Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work

Reference 44

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Observation c11cf5be-a101-4e87-81f3-d524468d7cb6 · outbound

This paper cites Asymptotic self-similar blow-up profile for three-dimensional axisymmetric euler equations using neural networks, 2023.

Automated discovery of finite volume schemes using Graph Neural Networks Asymptotic self-similar blow-up profile for three-dimensional axisymmetric euler equations using neural networks, 2023

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.177838Z

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Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work

Reference 46

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Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work

Reference 47

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This paper cites Finally, we use those inequalities to bound the error on a general graph.

Automated discovery of finite volume schemes using Graph Neural Networks Finally, we use those inequalities to bound the error on a general graph

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.132883Z

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Automated discovery of finite volume schemes using Graph Neural Networks Unresolved cited work

Reference 49

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Observation d99becac-a64d-460a-855b-393655d2ce5b · outbound

This paper cites This leads to Theorem C.5 where we demonstrate that a small training error requires at least 2mL` 2m` 4 parameters.

Automated discovery of finite volume schemes using Graph Neural Networks This leads to Theorem C.5 where we demonstrate that a small training error requires at least 2mL` 2m` 4 parameters

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.104016Z

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Observation 3cfa753a-5284-48b1-9cc8-db2ec909e9b4 · outbound

This paper cites C.1 Sparsity consideration Lemma C.1.

Automated discovery of finite volume schemes using Graph Neural Networks C.1 Sparsity consideration Lemma C.1

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.088630Z

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Observation 56a73736-d43e-48dd-9f6c-f53f57e38c90 · outbound

This paper cites W a p “ » ———– 1 0 0.

Automated discovery of finite volume schemes using Graph Neural Networks W a p “ » ———– 1 0 0

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:06:44.073699Z

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