Paper Citation Record · LEDGER

Inf-Sup Neural Networks for High Dimensional PDEs

As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2607.11718.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.11718 v1

Coverage vector

measured 62 of 62 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-14T03:39:49.437017Z

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Reference resolution

62 of 62 outbound references displayed

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Outbound references

Observation 2678ca35-0f0c-45b6-81eb-07c9b8118a00 · outbound

This paper cites 3, 499–541.

Inf-Sup Neural Networks for High Dimensional PDEs 3, 499–541

Reference 1

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 2

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Observation faf2b181-db57-45fd-959f-129cfec20912 · outbound

This paper cites 6, C688–C715.

Inf-Sup Neural Networks for High Dimensional PDEs 6, C688–C715

Reference 3

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Observation 3f924918-b25f-419d-bb0d-f64f81ef72d8 · outbound

This paper cites Boffi and Eric Vanden-Eijnden,Probability flow solution of the Fokker–Planck equation, Machine Learning: Science and Technology4(2023), no.

Inf-Sup Neural Networks for High Dimensional PDEs Boffi and Eric Vanden-Eijnden,Probability flow solution of the Fokker–Planck equation, Machine Learning: Science and Technology4(2023), no

Reference 4

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Observation efcfefc0-0be0-439f-9cef-b15117b6d432 · outbound

This paper cites Siegel,Convergence and error control of consistent PINNs for elliptic PDEs, IMA Journal of Numerical Analysis46(2025), no.

Inf-Sup Neural Networks for High Dimensional PDEs Siegel,Convergence and error control of consistent PINNs for elliptic PDEs, IMA Journal of Numerical Analysis46(2025), no

Reference 5

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Observation 7ae85b7d-34e5-49f0-b7fd-709ecc9626dc · outbound

This paper cites Caflisch,Monte Carlo and quasi-Monte Carlo methods, Acta Numerica7(1998), 1–49.

Inf-Sup Neural Networks for High Dimensional PDEs Caflisch,Monte Carlo and quasi-Monte Carlo methods, Acta Numerica7(1998), 1–49

Reference 6

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Observation fc7ca251-4767-44e0-b732-c3da7658048b · outbound

This paper cites 4, 303–314.

Inf-Sup Neural Networks for High Dimensional PDEs 4, 303–314

Reference 7

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Observation adf7ecb5-beb0-47e6-adb8-457ba08ae5b3 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 8

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Observation 7fa2e806-b457-4283-b517-6a84ba3460b4 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 9

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Observation 36b816ab-1227-4606-8763-0fb7aa566c24 · outbound

This paper cites 2, 811–841.

Inf-Sup Neural Networks for High Dimensional PDEs 2, 811–841

Reference 10

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Observation 52c5b947-5ef3-4757-b48f-437e3706cfb9 · outbound

This paper cites 3, 195– 201.

Inf-Sup Neural Networks for High Dimensional PDEs 3, 195– 201

Reference 11

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Observation 5b1034ad-706e-40db-a329-bdec98c57ff0 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 12

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Observation 57ebd799-708a-412d-a57b-8a130b9418e4 · outbound

This paper cites 6185–6215.

Inf-Sup Neural Networks for High Dimensional PDEs 6185–6215

Reference 13

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Observation e25baa16-66e7-44a6-b174-df7d40937d2a · outbound

This paper cites 32 ZIREN CHEN AND HAILIANG LIU.

Inf-Sup Neural Networks for High Dimensional PDEs 32 ZIREN CHEN AND HAILIANG LIU

Reference 14

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Observation ab54454f-84f1-4296-a3fc-af78ec0fa6cb · outbound

This paper cites Karniadakis, and Kenji Kawaguchi,Score-based physics- informed neural networks for high-dimensional Fokker–Planck equations, SIAM Journal on Scientific Computing47(2025), no.

Inf-Sup Neural Networks for High Dimensional PDEs Karniadakis, and Kenji Kawaguchi,Score-based physics- informed neural networks for high-dimensional Fokker–Planck equations, SIAM Journal on Scientific Computing47(2025), no

Reference 15

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Observation 677f30a3-1023-4c86-a92a-1165b3bb9436 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 16

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Observation 6491bec8-8b3e-4c27-b256-8b68e64b7622 · outbound

This paper cites Jagtap and George E.

Inf-Sup Neural Networks for High Dimensional PDEs Jagtap and George E

Reference 17

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Observation c1d72cd0-a617-4090-a58f-9367b74fc9d0 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 18

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Observation 21b8f0d3-6868-4039-83f2-cdf3e3073d9d · outbound

This paper cites 4880–4889.

Inf-Sup Neural Networks for High Dimensional PDEs 4880–4889

Reference 19

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Observation 07e90135-3bc8-4702-a33d-b32c3d12290f · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 20

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Observation 43a802a4-4bbd-4ffe-89ee-26e416ea398f · outbound

This paper cites Karniadakis,Variational physics-informed neu- ral networks for solving partial differential equations, 2019.

Inf-Sup Neural Networks for High Dimensional PDEs Karniadakis,Variational physics-informed neu- ral networks for solving partial differential equations, 2019

Reference 21

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Observation a4b290d6-58f9-4c56-9ae9-c3b50c245d7c · outbound

This paper cites Mahoney,Charac- terizing possible failure modes in physics-informed neural networks, Advances in Neural Information Processing Systems, vol.

Inf-Sup Neural Networks for High Dimensional PDEs Mahoney,Charac- terizing possible failure modes in physics-informed neural networks, Advances in Neural Information Processing Systems, vol

Reference 22

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Observation 855a2d69-9c47-48b9-a14b-26c12ed86f54 · outbound

This paper cites Lagaris, Aristidis Likas, and Dimitrios I.

Inf-Sup Neural Networks for High Dimensional PDEs Lagaris, Aristidis Likas, and Dimitrios I

Reference 23

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Observation 00336fdd-75b6-42b6-894f-8bce59043f9d · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 24

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Observation a7a2308f-505f-49d5-9c86-5dcc7175d2c1 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 25

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Observation b61b959f-409f-4b86-bf6b-7c66349d33b3 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 26

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Observation 12a02349-9117-45a2-af5b-7d72ba0ba8da · outbound

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

Inf-Sup Neural Networks for High Dimensional PDEs Fourier Neural Operator for Parametric Partial Differential Equations

Reference 27

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Observation 3021f0c7-e8e4-4b7b-93e5-2a81fe115d25 · outbound

This paper cites 6083–6093.

Inf-Sup Neural Networks for High Dimensional PDEs 6083–6093

Reference 28

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Observation 6b3763f1-9759-4d3c-93d9-576959ec1d62 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 29

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Observation b16c559b-5312-46df-b3c0-718807bd1da8 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 30

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Observation 9befd20f-1031-4745-99bf-34a85496e6e2 · outbound

This paper cites 3, 1385–1449.

Inf-Sup Neural Networks for High Dimensional PDEs 3, 1385–1449

Reference 31

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Observation ecaea0ce-88d6-493b-b403-f550c2f3f52e · outbound

This paper cites A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations.

Inf-Sup Neural Networks for High Dimensional PDEs A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations

Reference 32

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 33

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Observation 839db060-b282-4329-8d74-cc18f6bc75b9 · outbound

This paper cites Karniadakis,Learning non- linear operators via DeepONet based on the universal approximation theorem of operators, Nature Machine Intelligence3(2021), no.

Inf-Sup Neural Networks for High Dimensional PDEs Karniadakis,Learning non- linear operators via DeepONet based on the universal approximation theorem of operators, Nature Machine Intelligence3(2021), no

Reference 34

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Observation a5968af5-7fc2-4661-9a78-5cc2b6ede356 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs 3196–3241

Reference 35

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Inf-Sup Neural Networks for High Dimensional PDEs McClenny and Ulisses M

Reference 36

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 37

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 38

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Observation 481dfd7d-b30c-432c-8088-0fa82b661d4a · outbound

This paper cites A priori error analysis of consistent PINNs for parabolic PDEs.

Inf-Sup Neural Networks for High Dimensional PDEs A priori error analysis of consistent PINNs for parabolic PDEs

Reference 39

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 40

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Observation 645824b3-57f0-40d0-bdb7-377e3748d97c · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs 34, 2021, pp

Reference 41

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Inf-Sup Neural Networks for High Dimensional PDEs 1, 229–251

Reference 42

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

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Inf-Sup Neural Networks for High Dimensional PDEs 5, 55–66

Reference 45

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

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Inf-Sup Neural Networks for High Dimensional PDEs 36, 2023, pp

Reference 47

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 48

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 49

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Inf-Sup Neural Networks for High Dimensional PDEs 4, 507–554

Reference 50

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 51

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

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Inf-Sup Neural Networks for High Dimensional PDEs 5, A3055–A3081

Reference 53

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Inf-Sup Neural Networks for High Dimensional PDEs 40, eabi8605

Reference 54

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 55

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Observation a83f61f5-0bc0-4c3d-89d3-0732c1e734d1 · outbound

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Inf-Sup Neural Networks for High Dimensional PDEs 1, 360–395

Reference 56

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Inf-Sup Neural Networks for High Dimensional PDEs 9, 10629–10636

Reference 57

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Observation d531bf21-1fcd-469d-a302-0cb836b60bb4 · outbound

This paper cites Karniadakis,Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems, Computer Methods in Applied Mechanics and Engineering393(2022), 114823.

Inf-Sup Neural Networks for High Dimensional PDEs Karniadakis,Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems, Computer Methods in Applied Mechanics and Engineering393(2022), 114823

Reference 58

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Reference 59

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

Reference 60

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Observation d09c80e5-8f6d-4014-8007-47b014bdc275 · outbound

This paper cites Simulating Fokker-Planck equations via mean field control of score-based normalizing flows.

Inf-Sup Neural Networks for High Dimensional PDEs Simulating Fokker-Planck equations via mean field control of score-based normalizing flows

Reference 61

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Inf-Sup Neural Networks for High Dimensional PDEs Unresolved cited work

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Pith citing papers

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