Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy | Paper Citation Record

Observation 16e6297d-0f34-42df-a055-40e8d15412b9 · outbound

This paper cites Traveling wave solutions in a model for social outbursts in a tension-inhibitive regime.Studies in Applied Mathematics, 147(2):650–674, 2021.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Traveling wave solutions in a model for social outbursts in a tension-inhibitive regime.Studies in Applied Mathematics, 147(2):650–674, 2021

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 2

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This paper cites Bastos, Dan Mercea, and Arthur Charpentier.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Bastos, Dan Mercea, and Arthur Charpentier

Reference 3

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This paper cites Berestycki and J.-P.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Berestycki and J.-P

Reference 4

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This paper cites A model of riots dynamics: Shocks, diffusion and thresholds.Networks and Heterogeneous Media, 10(3):443–475, July 2015.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy A model of riots dynamics: Shocks, diffusion and thresholds.Networks and Heterogeneous Media, 10(3):443–475, July 2015

Reference 5

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This paper cites Gordon, Sebastian Roch ´e, Nancy Rodriguez, and Jean-Pierre Nadal.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Gordon, Sebastian Roch ´e, Nancy Rodriguez, and Jean-Pierre Nadal

Reference 6

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Observation d74db5e0-557d-48b1-a940-6411b2176386 · outbound

This paper cites Bortz, Daniel A.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Bortz, Daniel A

Reference 7

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This paper cites Bortz, Daniel A.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Bortz, Daniel A

Reference 8

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This paper cites Brunton, Joshua L.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Brunton, Joshua L

Reference 9

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Observation 178a931d-ab7d-4495-942a-269ad9e17601 · outbound

This paper cites Burbeck, Walter J.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Burbeck, Walter J

Reference 10

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Observation 8ca6af34-4ba4-4422-b1b4-738b625b4359 · outbound

This paper cites Bortz, and Vanja Dukic.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Bortz, and Vanja Dukic

Reference 11

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This paper cites Darling and J.R.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Darling and J.R

Reference 12

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This paper cites Davies, Hannah M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Davies, Hannah M

Reference 13

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Observation 87980dd5-e611-4680-b839-b5e44f7a1ae9 · outbound

This paper cites Number of internet and social media users worldwide as of October 2025.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Number of internet and social media users worldwide as of October 2025

Reference 14

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 15

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This paper cites Ethier and Thomas G.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Ethier and Thomas G

Reference 16

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 17

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This paper cites Autonomous inference of complex network dynamics from incomplete and noisy data.Nature Computational Science, 2(3):160–168, March 2022.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Autonomous inference of complex network dynamics from incomplete and noisy data.Nature Computational Science, 2(3):160–168, March 2022

Reference 18

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This paper cites Bortz, and Youngsoo Choi.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Bortz, and Youngsoo Choi

Reference 19

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 20

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Observation 9bbad74b-9c92-45b6-9cfd-a663ddb7a409 · outbound

This paper cites Digital 2026 Global Overview Report.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Digital 2026 Global Overview Report

Reference 21

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This paper cites Social media and protests: An examination of Twitter images of the 2011 Egyptian revolution.New Media & Society, 18(9):1973–1992, 2016.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Social media and protests: An examination of Twitter images of the 2011 Egyptian revolution.New Media & Society, 18(9):1973–1992, 2016

Reference 22

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This paper cites Kiss, Joel C.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Kiss, Joel C

Reference 23

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Observation 3d47d26e-e1a9-4a65-ae2d-b6aa14889a47 · outbound

This paper cites Lagergren, John T.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Lagergren, John T

Reference 24

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This paper cites Learning interaction kernels in mean-field equations of first-order systems of interacting particles.SIAM Journal on Scientific Computing, 44(1):A260–A285, 2022.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Learning interaction kernels in mean-field equations of first-order systems of interacting particles.SIAM Journal on Scientific Computing, 44(1):A260–A285, 2022

Reference 25

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 26

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 27

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This paper cites Learning interpretable closures for thermal radiation transport in optically-thin media using WSINDy, 2025.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Learning interpretable closures for thermal radiation transport in optically-thin media using WSINDy, 2025

Reference 28

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This paper cites Messenger and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger and David M

Reference 29

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This paper cites Messenger and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger and David M

Reference 30

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This paper cites Messenger and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger and David M

Reference 31

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Observation e4efb3e0-d853-4c17-bf17-dfb434fe0a2d · outbound

This paper cites Asymptotic consistency of the WSINDy algorithm in the limit of continuum data.IMA Journal of Numerical Analysis, page drae086, December 2024.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Asymptotic consistency of the WSINDy algorithm in the limit of continuum data.IMA Journal of Numerical Analysis, page drae086, December 2024

Reference 32

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This paper cites Messenger, Joshua W.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, Joshua W

Reference 33

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This paper cites Messenger, Emiliano Dall’ Anese, and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, Emiliano Dall’ Anese, and David M

Reference 34

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This paper cites Messenger, April Tran, Vanja Dukic, and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, April Tran, Vanja Dukic, and David M

Reference 35

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This paper cites Minor, B.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Minor, B

Reference 36

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This paper cites Messenger, Vanja Dukic, and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, Vanja Dukic, and David M

Reference 37

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This paper cites Hawkes binomial topic model with applications to coupled conflict-Twitter data.The Annals of Applied Statistics, 14(4):1984–2002, 2020.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Hawkes binomial topic model with applications to coupled conflict-Twitter data.The Annals of Applied Statistics, 14(4):1984–2002, 2020

Reference 38

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This paper cites Learning opinion dynamics from social traces.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Learning opinion dynamics from social traces

Reference 39

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 40

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This paper cites Vixie, Arghavan Talebanpour, and Yunfeng Hu.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Vixie, Arghavan Talebanpour, and Yunfeng Hu

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This paper cites Epidemic dynamics and endemic states in complex networks.Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 63(6):066117, May 2001.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Epidemic dynamics and endemic states in complex networks.Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 63(6):066117, May 2001

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This paper cites Lindstrom, Christian Parkinson, Chuntian Wang, Andrea L.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Lindstrom, Christian Parkinson, Chuntian Wang, Andrea L

Reference 43

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Observation c6c4d8f4-7675-4092-9bfd-e12d4c26ab49 · outbound

This paper cites Frontiers in Applied Dynamical Systems: Reviews and Tutorials.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Frontiers in Applied Dynamical Systems: Reviews and Tutorials

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This paper cites Predicting network dynamics without requiring the knowledge of the interaction graph.Proceedings of the National Academy of Sciences, 119(44):e2205517119, November 2022.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Predicting network dynamics without requiring the knowledge of the interaction graph.Proceedings of the National Academy of Sciences, 119(44):e2205517119, November 2022

Reference 45

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This paper cites Proctor, Steven L.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Proctor, Steven L

Reference 46

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Observation b27ac7d1-8c97-4fac-97d2-85c9d1d8dad8 · outbound

This paper cites Jeffrey Brantingham, and Nancy Rodr´ıguez.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Jeffrey Brantingham, and Nancy Rodr´ıguez

Reference 47

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Observation daa1a1b3-3d2f-4fc6-9494-5f871de12a69 · outbound

This paper cites Jeffrey Brantingham, and Nancy Rodr´ıguez.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Jeffrey Brantingham, and Nancy Rodr´ıguez

Reference 48

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This paper cites Weak Form Scientific Machine Learning: Test Function Construction for System Identification.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Weak Form Scientific Machine Learning: Test Function Construction for System Identification

Reference 49

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Observation d910cfad-9845-4adb-bc75-0a274c36dc75 · outbound

This paper cites Messenger, Youngsoo Choi, and David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, Youngsoo Choi, and David M

Reference 50

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This paper cites Messenger, David M.

Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Messenger, David M

Reference 51

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Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy Unresolved cited work

Reference 52

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