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Paper Citation Record · LEDGER

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

As of 18 August 2026, this Paper Citation Record lists 100 of 124 outbound references and 0 inbound Pith citation observations for arXiv:2607.14233.

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

pith.paper-citation-record.v1
2607.14233 v1

Coverage vector

measured 100 of 124 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:48:56.887214Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 124 outbound references displayed

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  • verified fuzzy0
  • unresolved99
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External citation measurements

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Outbound references

Observation 9dd6c96b-e4ed-49a7-a7d0-d12e6ea219ad · outbound

This paper cites How to initialize your network? robust initialization for weightnorm & resnets.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks How to initialize your network? robust initialization for weightnorm & resnets

Reference 1

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Observation 52a26cf2-26ae-447f-80e7-98e1cb7a2bb6 · outbound

This paper cites Hypernetwork-based meta-learning for low-rank physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Hypernetwork-based meta-learning for low-rank physics-informed neural networks

Reference 3

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source=arxiv_source observed=2026-08-02T02:48:47.978350Z digest=sha256:02276d0c93179c13278a88c19c042de7dc31a92acb0765344303599eb22ea504

Observation 2ba84097-5547-4ce9-8a9f-9ae565b70fc1 · outbound

This paper cites Scientific machine learning through physics--informed neural networks: Where we are and what’s next.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Scientific machine learning through physics--informed neural networks: Where we are and what’s next

Reference 4

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source=arxiv_source observed=2026-08-02T02:48:48.067417Z digest=sha256:43de41e68c33dd06ef997b9f3eb6958efa4c2897b0b2394220b08103061aed8a

Observation feae99e8-2ffd-4797-a187-84ff59c9eabc · outbound

This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 5

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source=arxiv_source observed=2026-08-02T02:48:48.181668Z digest=sha256:9afee7dcf44284c7cb62e16a5098eb3fb033ad7e22aeb53d96cd342e8221a34f

Observation 4c363a8f-068d-404d-b51e-b0db72153ec5 · outbound

This paper cites Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling

Reference 6

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source=arxiv_source observed=2026-08-02T02:48:48.300518Z digest=sha256:ebd396b6fe1a6f4b9ab420c3d1e8437829a20f42d4f467c6a9112f0c89162231

Observation a758b184-f45d-49ef-850c-1ede7ecedc1b · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 7

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source=arxiv_source observed=2026-08-02T02:48:48.388571Z digest=sha256:a8aba17992461accc26b8d0ee30b2fc0a939970d9fca28120c37d47a30551216

Observation a3ec071d-fd38-4c7d-92a3-0cfd07a72c37 · outbound

This paper cites Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel

Reference 8

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source=arxiv_source observed=2026-08-02T02:48:48.477817Z digest=sha256:8765e8e388783ea7bbe27566b0eca147894db976cb9170bfce863737694a1874

Observation e060d9a5-7c8b-47d9-bdce-6798cdfab03f · outbound

This paper cites Flat minima.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Flat minima

Reference 9

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Observation 1d42d971-00a7-4b27-a91f-fc01f18b5559 · outbound

This paper cites Dpm: A novel training method for physics-informed neural networks in extrapolation.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Dpm: A novel training method for physics-informed neural networks in extrapolation

Reference 10

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source=arxiv_source observed=2026-08-02T02:48:48.670989Z digest=sha256:757c8954b3a986f5dd5191da9d31115b3f40e525876bcef0bdb67576b6344933

Observation 912def68-4edf-462d-901c-46bfc13b421e · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Characterizing possible failure modes in physics-informed neural networks

Reference 11

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Observation 70a14a70-7003-4f60-8ea0-2d018e89bb22 · outbound

This paper cites Physics informed neural networks for fluid flow analysis with repetitive parameter initialization.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Physics informed neural networks for fluid flow analysis with repetitive parameter initialization

Reference 13

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Observation dfdb8771-6634-4ebf-b8af-bda2adacafcb · outbound

This paper cites Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh

Reference 15

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Observation 0eb52880-f5b4-440b-acf4-ef92839e3887 · outbound

This paper cites Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

Reference 16

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Observation 82cc9c50-c82a-426d-843b-ffe75da54c3f · outbound

This paper cites Numerical gaussian processes for time-dependent and nonlinear partial differential equations.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Numerical gaussian processes for time-dependent and nonlinear partial differential equations

Reference 18

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Observation f26bbd17-ffde-48e6-828d-227a9040d46d · outbound

This paper cites Meta-learning with implicit gradients.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Meta-learning with implicit gradients

Reference 19

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Observation 687bba97-f02e-4a16-8723-2c3e8fbf8d48 · outbound

This paper cites Dats: Difficulty-aware task sampler for meta-learning physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Dats: Difficulty-aware task sampler for meta-learning physics-informed neural networks

Reference 22

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Observation 04a492e3-99ed-4956-ae03-24b919a5bf47 · outbound

This paper cites Zippered polygon meshes from range images.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Zippered polygon meshes from range images

Reference 23

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Observation 41fe49cc-e0d0-4260-8325-22c289261313 · outbound

This paper cites Understanding and mitigating gradient flow pathologies in physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Reference 24

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Observation 99356bda-d23d-4ae1-9238-e47d2019d67a · outbound

This paper cites On the eigenvector bias of fourier feature networks: From regression to solving multi-scale pdes with physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks On the eigenvector bias of fourier feature networks: From regression to solving multi-scale pdes with physics-informed neural networks

Reference 25

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Observation 166e61e2-9fe8-4a8c-9c92-0b2244e8c571 · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks When and why pinns fail to train: A neural tangent kernel perspective

Reference 27

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Observation 5f802826-db4b-4d38-b0e9-b61dd41d179e · outbound

This paper cites Gradient alignment in physics-informed neural networks: A second-order optimization perspective, 2025.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Gradient alignment in physics-informed neural networks: A second-order optimization perspective, 2025

Reference 28

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Observation 121e186a-4d39-4795-a7f3-17442897976e · outbound

This paper cites A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 29

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Observation a14b54d0-a1e9-4ce8-a20e-ce27955d882a · outbound

This paper cites Mean field residual networks: On the edge of chaos.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Mean field residual networks: On the edge of chaos

Reference 31

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Observation 8ac90603-95fb-403c-983f-32022ee31e5d · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 32

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Observation c354054f-6d0c-46ae-8cc4-339ccba45fdf · outbound

This paper cites Classification Problem Solving.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Classification Problem Solving

Reference 33

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Observation f2d70799-6ead-462f-a015-e8fb8f98bfc9 · outbound

This paper cites , title =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks , title =

Reference 34

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Observation e9b818be-fd56-4d2d-bb23-a31a89ef6394 · outbound

This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 35

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Observation 5d2de18e-77e7-4a5b-ba4b-5c4c507d81b1 · outbound

This paper cites Clancey and Glenn Rennels , abstract =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Clancey and Glenn Rennels , abstract =

Reference 36

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Observation 0595e063-04c9-4c39-b144-1e452f175ac0 · outbound

This paper cites and Rennels, Glenn R.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Rennels, Glenn R

Reference 37

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source=arxiv_source observed=2026-08-02T02:48:51.218605Z digest=sha256:63db33a34e730380b876c0bb9a8b9511ae0e3942fb01a55386da0f6d1da57cd8

Observation 0be9d0b1-4954-4232-95c3-ccb635f54c23 · outbound

This paper cites Poligon: A System for Parallel Problem Solving.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Poligon: A System for Parallel Problem Solving

Reference 38

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source=arxiv_source observed=2026-08-02T02:48:51.306121Z digest=sha256:2414cb37af456ed8b19c6c2dfb161eaf41477938d7a0fd162322ddef5be0ff27

Observation 86514036-3c1c-4b9e-9f35-876d94daea0d · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 39

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Observation 517d59ed-5d22-4a61-ba8a-fb7db71c80cb · outbound

This paper cites The Engineering of Qualitative Models.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks The Engineering of Qualitative Models

Reference 40

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Observation ee7b6a48-720d-42e4-aa04-f91ee07253fd · outbound

This paper cites 2017 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2017 , eprint=

Reference 41

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Observation 1928d495-01cc-412f-8fed-f6ff54dc0f86 · outbound

This paper cites Pluto: The 'Other' Red Planet.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Pluto: The 'Other' Red Planet

Reference 42

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source=arxiv_source observed=2026-08-02T02:48:51.641074Z digest=sha256:c28374ce4bd2a9c870b0f0c9123c8be30417d00e8c1685db45ce526223762f34

Observation 76a5c3a9-bc97-4536-b461-b460da467d57 · outbound

This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 43

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source=arxiv_source observed=2026-08-02T02:48:51.733220Z digest=sha256:6f27caa2f55dea491ba2b6a099300da2488ab334d4241020595a6a581c859935

Observation f210a63c-a0f0-492f-8ac9-987fd96910b2 · outbound

This paper cites PINNACLE: PINN Adaptive ColLocation and Experimental points selection.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks PINNACLE: PINN Adaptive ColLocation and Experimental points selection

Reference 44

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no resolver link, observed 2026-08-02T02:48:51.785111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:51.785111Z digest=sha256:d3806172638a5919757449cbbeabf240c2099bdf02291ceee9bb0e6986b814a6

Observation 031cdcf4-0856-4b70-99bc-88b137d56a38 · outbound

This paper cites Journal of Machine Learning Research , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of Machine Learning Research , volume=

Reference 45

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unresolved
no resolver link, observed 2026-08-02T02:48:51.838385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:51.838385Z digest=sha256:3d6353cc78f245fc5a51810bcba2295983f8099390dafc5748ecb27081ec83c8

Observation 02d6ed21-24b6-487a-9393-ae1e037d5464 · outbound

This paper cites Knowledge-guided Machine Learning: Current Trends and Future Prospects.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Knowledge-guided Machine Learning: Current Trends and Future Prospects

Reference 46

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unresolved
no resolver link, observed 2026-08-02T02:48:51.920546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:51.920546Z digest=sha256:8e65436eb596b5b29b51250d3b022eff962a346f0fd18c6bb20687dbc2ded3bc

Observation 7f235c89-1496-48b4-bbff-59b74e7ee8bd · outbound

This paper cites and Zwart, Jacob A.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Zwart, Jacob A

Reference 47

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unresolved
no resolver link, observed 2026-08-02T02:48:51.982910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:51.982910Z digest=sha256:54c78b265a654574904a9c458d48cab8ae7cc2b97bcffeea1e0650c3fc4b412d

Observation 95f8ed8e-152b-4aa9-9cb1-f4f1caca404d · outbound

This paper cites Knowledge Guided Machine Learning , pages=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Knowledge Guided Machine Learning , pages=

Reference 48

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unresolved
no resolver link, observed 2026-08-02T02:48:52.052372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.052372Z digest=sha256:f06dc7918b9151c5297c6fabe507871f68524f24ca8fc68cabfef6d482216dda

Observation f7c11a82-4de2-494c-ade4-3f6db02f3659 · outbound

This paper cites and Steinbach, Michael and Banerjee, Arindam and Ganguly, Auroop and Shekhar, Shashi and Samatova, Nagiza and Kumar, Vipin , journal=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Steinbach, Michael and Banerjee, Arindam and Ganguly, Auroop and Shekhar, Shashi and Samatova, Nagiza and Kumar, Vipin , journal=

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.137055Z digest=sha256:89166cf05fb7e56a84c61e8d6a1471f1932cf6ee18053f36e3a3fb2dadc64264

Observation 5ae9eec9-0dd5-40c9-a5f3-f7d5a9bc5fce · outbound

This paper cites 2022 , publisher=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2022 , publisher=

Reference 50

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no resolver link, observed 2026-08-02T02:48:52.215118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.215118Z digest=sha256:4ab91f5e4e6e05d6d348eb1305d1baff44904862c048f5b46756018921766e1a

Observation 8208584d-d976-4572-8e28-17c6d11fc9bd · outbound

This paper cites , volume =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks , volume =

Reference 51

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unresolved
no resolver link, observed 2026-08-02T02:48:52.292264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.292264Z digest=sha256:27a1c1174461df02285884dc016e104691c05f00ca33fb4fcd28c16b546a6795

Observation b94d2328-4471-463c-b66c-ad52ce10f8d5 · outbound

This paper cites an unresolved cited work.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Unresolved cited work

Reference 52

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unresolved
no resolver link, observed 2026-08-02T02:48:52.373224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.373224Z digest=sha256:6d029d31ae2edbf79e60e58fd835dfeb287a3eed0551b4ad6e09c290bf42fd24

Observation 80b61d39-1813-4fb6-b7b1-a47b6b6a3229 · outbound

This paper cites Publication manual of the American Psychological Association , isbn =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Publication manual of the American Psychological Association , isbn =

Reference 53

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unresolved
no resolver link, observed 2026-08-02T02:48:52.448488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.448488Z digest=sha256:bcf73ca15750406b43d7b3c0deda5ead252bf28e04e095df9e9eb098be691e81

Observation dc8b4b23-8e3e-493a-b710-6b1378e60cd3 · outbound

This paper cites The Manual of Scientific Style , url =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks The Manual of Scientific Style , url =

Reference 54

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no resolver link, observed 2026-08-02T02:48:52.525863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.525863Z digest=sha256:270a7502a24c44771a290a317838bace5b15e5f54014b73b20ae3d801d9c8c5e

Observation 76fb7e11-ec2a-4ca0-bfca-f23f0872765d · outbound

This paper cites Crisis Management in the Food and Drinks Industry , url =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Crisis Management in the Food and Drinks Industry , url =

Reference 55

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unresolved
no resolver link, observed 2026-08-02T02:48:52.609323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.609323Z digest=sha256:d9cd80ecd7ff0c75e96729ed9dc8144cfb485079d7b0f0a10a80147d378bdd0e

Observation bb96be00-04c9-4e52-aaed-b58ba5057c48 · outbound

This paper cites Incorporating Prior Domain Knowledge into Deep Neural Networks , year=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Incorporating Prior Domain Knowledge into Deep Neural Networks , year=

Reference 56

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unresolved
no resolver link, observed 2026-08-02T02:48:52.681454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.681454Z digest=sha256:d62eea0235770533c99c7ce85b1eccb55ef5012159e8df8dce7c0138017a97d3

Observation 9f1fc3dc-8931-470f-a3e2-4a8e1c3ce487 · outbound

This paper cites and Gholami, Amir and Zhe, Shandian and Kirby, Robert M.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Gholami, Amir and Zhe, Shandian and Kirby, Robert M

Reference 57

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no resolver link, observed 2026-08-02T02:48:52.742512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.742512Z digest=sha256:110c56a4fb6a44148b6fb17f1e81e062e4bf617a6ccec24c0873fb88a3faa333

Observation 9d018cb3-116f-4f79-9c8d-97d2401dd372 · outbound

This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Reference 58

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no resolver link, observed 2026-08-02T02:48:52.809437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.809437Z digest=sha256:44cac7a7bf5305e911752181ba2f215f5f5f73b65b2be704d902ddc0b63d88a6

Observation 0842dda1-9592-4d32-9138-4e92b25f50ff · outbound

This paper cites Deep Information Propagation.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Deep Information Propagation

Reference 59

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unresolved
no resolver link, observed 2026-08-02T02:48:52.886634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.886634Z digest=sha256:d37d695a17781485ffb19a58549de69e459be0e835e4c23e1b3e0709a40f105c

Observation f4671bac-6314-430c-9214-3e1f9212b42c · outbound

This paper cites Journal of computational physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of computational physics , volume=

Reference 60

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unresolved
no resolver link, observed 2026-08-02T02:48:52.961852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:52.961852Z digest=sha256:e759bd021ce8e8ddbb679e43d06439374fccdab1316d7edb44afaa1fa2433735

Observation 803f3840-c735-4928-a5f2-ef9c1dfec7ef · outbound

This paper cites Journal of Computational Physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of Computational Physics , volume=

Reference 61

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no resolver link, observed 2026-08-02T02:48:53.036089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.036089Z digest=sha256:129a39de56252c3fa2a19a7f5e0bff94d1c20d1a45c8fe0005799cfbfd479697

Observation a316edab-4dfb-4cd6-85ce-74feb67b2d8c · outbound

This paper cites SIAM Journal on Scientific Computing , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks SIAM Journal on Scientific Computing , volume=

Reference 62

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no resolver link, observed 2026-08-02T02:48:53.111752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.111752Z digest=sha256:92e1281e50fc9fbae4ca3859a46878afdc53c56395f2e7fe3f8180b7a3cf03e6

Observation 0faac986-005a-4ab0-9642-aa5ab9fb0a2f · outbound

This paper cites 2023 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2023 , eprint=

Reference 63

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no resolver link, observed 2026-08-02T02:48:53.213560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.213560Z digest=sha256:aadf42187c793b738a05dd94279eeb213397730c171c3e8e7683b56e914c77ac

Observation 60597387-8d24-4732-b3f8-4828942a36f9 · outbound

This paper cites Meta-learning PINN loss functions , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Meta-learning PINN loss functions , volume=

Reference 65

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.404320Z digest=sha256:6f748a7978d064013c62a0c618d687ec20c98273df7cbae9cafd91bf4ed3f6f6

Observation f85a1e74-9901-45ee-91d5-ca323e0063e9 · outbound

This paper cites A novel meta-learning initialization method for physics-informed neural networks , volume =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks A novel meta-learning initialization method for physics-informed neural networks , volume =

Reference 66

Resolution
verified exact
doi, observed 2026-08-02T02:53:30.441064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-02T02:48:53.520470Z digest=sha256:8101a3e0290a36e18bf1775cc4f20abdf02685636dbe897a68ef9afaad22dc80

Observation 23f81936-1913-41b8-8050-f60cff831f17 · outbound

This paper cites , urldate =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks , urldate =

Reference 67

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no resolver link, observed 2026-08-02T02:48:53.615220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.615220Z digest=sha256:f21c792d1f0094e5ac029285c5ea3880d1d5fb88280d850e40367f2597ee6b22

Observation 468bf253-e948-4e3e-9e23-4015fae67bd4 · outbound

This paper cites On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type

Reference 68

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no resolver link, observed 2026-08-02T02:48:53.671949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.671949Z digest=sha256:941637ea12808417963cdb475b8d099415b9f3482dfca3afe13b8b0a7ac589f4

Observation d6b0923b-6817-42dc-8760-c126457abea5 · outbound

This paper cites Gradient-enhanced physics-informed neural networks for forward and inverse.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Gradient-enhanced physics-informed neural networks for forward and inverse

Reference 69

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no resolver link, observed 2026-08-02T02:48:53.757049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.757049Z digest=sha256:beb05126a7762571f274e3b7e871f76ac8ad6f7736c40723dafd38a99fec960d

Observation f2e5b446-e93b-4e7d-8bc9-85dc743070a5 · outbound

This paper cites 2017 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2017 , eprint=

Reference 70

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no resolver link, observed 2026-08-02T02:48:53.843571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.843571Z digest=sha256:6afd253cecfaf1dc5b5638ee82e629089f09ff3e89c104ef95217692884aa861

Observation 9f12f91d-8a64-421a-b6cf-5f1a07ef8541 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Advances in Neural Information Processing Systems , volume=

Reference 71

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no resolver link, observed 2026-08-02T02:48:53.939553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:53.939553Z digest=sha256:56286769c4ed3b5a93eb88bbdf6c156eb26ee58b2de363d727a48908fe4e9e15

Observation 0ec7c6a4-1bc5-4d52-aa7d-c2f575be4715 · outbound

This paper cites an unresolved cited work.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Unresolved cited work

Reference 72

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unresolved
no resolver link, observed 2026-08-02T02:48:54.019987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.019987Z digest=sha256:e189ec68366e770453b423d30e7f5d084d46d52927187a3ecaebcb9d07124c57

Observation 76a2c832-68f9-440a-bdab-283bdb7740e1 · outbound

This paper cites an unresolved cited work.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Unresolved cited work

Reference 73

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no resolver link, observed 2026-08-02T02:48:54.188114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.188114Z digest=sha256:4a733f72e94a8ecbed3b325d9453db20541f5a87922c05ff13ec81dea3e141b6

Observation 290bee44-d681-4b74-8f5e-89e529b1bedd · outbound

This paper cites and Toscano, Juan Diego and Stergiopulos, Nikolaos and Karniadakis, George Em , urldate =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Toscano, Juan Diego and Stergiopulos, Nikolaos and Karniadakis, George Em , urldate =

Reference 74

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no resolver link, observed 2026-08-02T02:48:54.290457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.290457Z digest=sha256:54c5b9cc0579992211f3de11a514829f3d5f30601d30040d512b29f0f70c44ad

Observation 6f16d8f7-7775-4f38-a01b-6fc52d2a8401 · outbound

This paper cites and Tegmark, Max , urldate =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks and Tegmark, Max , urldate =

Reference 75

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unresolved
no resolver link, observed 2026-08-02T02:48:54.397258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.397258Z digest=sha256:8b06279fe02c5770d86556126cca229a876090f377c8c5f90f6114c0e224fba4

Observation ac0af712-acca-49d5-a48f-50e9de44e914 · outbound

This paper cites 2022 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2022 , eprint=

Reference 76

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unresolved
no resolver link, observed 2026-08-02T02:48:54.501473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.501473Z digest=sha256:3cd0e8dc38d6876fa39be60a4d22f488460ecab8645864f6e27c407229f6f5bc

Observation 2dabded1-3901-4821-8e1c-8a88e756b972 · outbound

This paper cites an unresolved cited work.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Unresolved cited work

Reference 77

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no resolver link, observed 2026-08-02T02:48:54.572286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.572286Z digest=sha256:eaacce4d69ad26ff02a403450b27c2534ee41e86eba314cac973193e611854f7

Observation 7335beba-5ca9-4ec4-9c23-3f529543a043 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 78

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unresolved
no resolver link, observed 2026-08-02T02:48:54.642033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.642033Z digest=sha256:bc6b8bdbb45bd122d86e3c0d056a9012eadb2402c02d8530f960aed69bba1190

Observation 97293701-c145-4960-a786-c49e39aa9ad0 · outbound

This paper cites 2024 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2024 , eprint=

Reference 79

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unresolved
no resolver link, observed 2026-08-02T02:48:54.803226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.803226Z digest=sha256:585a4ab23b0c59055ff650989165dcd63f41406b2ff3ea041cc217520569e697

Observation 67aa923d-6ce8-4dae-8463-cc40aaf513cb · outbound

This paper cites Advances in neural information processing systems , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Advances in neural information processing systems , volume=

Reference 80

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unresolved
no resolver link, observed 2026-08-02T02:48:54.877281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.877281Z digest=sha256:22e891bb4710f29a840134c9a79ba438a45e76d2a98d01feef32a8b8bbe94b04

Observation a5130b11-f480-47b0-bb19-2d1f8979a927 · outbound

This paper cites , urldate =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks , urldate =

Reference 81

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unresolved
no resolver link, observed 2026-08-02T02:48:54.944887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:48:54.944887Z digest=sha256:d6fdd85c6ff5a20105a0ec1c9351b501864a0cc5409bb044fff9e911ae1caa14

Observation 746a2c70-bc13-49ee-bec2-ae994534f228 · outbound

This paper cites Advances in neural information processing systems , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Advances in neural information processing systems , volume=

Reference 82

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source=arxiv_source observed=2026-08-02T02:48:55.043884Z digest=sha256:f98578c9e511b3bb8a642ce0e8e08099a960ebcc745ad42bc1f5a6b2000a39d7

Observation 18e2be49-eb14-49a3-a0da-725fd420368d · outbound

This paper cites 2017 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2017 , eprint=

Reference 83

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source=arxiv_source observed=2026-08-02T02:48:55.121593Z digest=sha256:044449ecb55eccdb81d3792942a47567689899106a8cde9b8d840b50c3ce6b1f

Observation 12db0513-a870-45f6-8d29-749a72aa22f9 · outbound

This paper cites Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given

Reference 84

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source=arxiv_source observed=2026-08-02T02:48:55.174402Z digest=sha256:8bb9bd87c58a9ffb41d200d3653744a90f1e3ba104d51102546e8d0acb6a6a7b

Observation b779259a-1814-48e7-9598-9a7549a87471 · outbound

This paper cites 2016 , eprint=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2016 , eprint=

Reference 85

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source=arxiv_source observed=2026-08-02T02:48:55.246474Z digest=sha256:584793fdac8ab6dc84590f6c8f1213ad144da6154f6585263da4b9edf2c23315

Observation 00fc2a1b-12a4-4a9a-96a9-848733a21127 · outbound

This paper cites Mean Field Theory and its application to deep learning , abstract =.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Mean Field Theory and its application to deep learning , abstract =

Reference 86

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source=arxiv_source observed=2026-08-02T02:48:55.304613Z digest=sha256:b0a53368bca71e97fed0fb5e9fd16137996908df73b6c3fd6b4043aee78ced87

Observation caff7f1b-b711-43a6-9190-473bb3d5dc1a · outbound

This paper cites Book III, Lemma V, Case , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Book III, Lemma V, Case , volume=

Reference 87

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source=arxiv_source observed=2026-08-02T02:48:55.406480Z digest=sha256:27c67c11a962e110b696c493e555407c309c53315b402132df3d82923ed6afac

Observation 351297b3-9b2d-4c36-9815-fc870a918cdd · outbound

This paper cites Reviews of Modern Physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Reviews of Modern Physics , volume=

Reference 88

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source=arxiv_source observed=2026-08-02T02:48:55.477106Z digest=sha256:5218831faca24cf4e037f4530d856ce153ae1284b59626ac022fd508cf4a484f

Observation 7c3cf5c9-e6b7-414e-bbf1-9c2d792f119b · outbound

This paper cites SIAM Journal on Scientific Computing , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks SIAM Journal on Scientific Computing , volume=

Reference 89

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source=arxiv_source observed=2026-08-02T02:48:55.587332Z digest=sha256:019e536fb1a11edb92cd2ec5d0efb1a342ac26f2280cbc6675e20241d386d0f0

Observation 98a6f1db-014f-4d99-a28c-8af83624ee0e · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Computer Methods in Applied Mechanics and Engineering , volume=

Reference 90

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source=arxiv_source observed=2026-08-02T02:48:55.646956Z digest=sha256:219e37a1f6239d86ba2a3f7dc773a2fda2344b8949b5d424e3fa0e06bf46b2c6

Observation c5555979-9dfc-4882-8449-ecb8643674a1 · outbound

This paper cites Respecting causality is all you need for training physics-informed neural networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Respecting causality is all you need for training physics-informed neural networks

Reference 91

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source=arxiv_source observed=2026-08-02T02:48:55.699909Z digest=sha256:95399bf75257452b0468f33c9816a6f781ea58e80b3334c6fd448bdea7ca00d1

Observation 0f90a2df-6df3-4cd3-9a69-75cbd86f5777 · outbound

This paper cites Optics express , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Optics express , volume=

Reference 92

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source=arxiv_source observed=2026-08-02T02:48:55.764537Z digest=sha256:1ce2f079a3176b6cc99bad49167c09923c1a30543cd7f52524ead6d7421b30fe

Observation f1e882ec-0033-42a8-9cf8-bb7fb15ca38e · outbound

This paper cites Journal of Computational Physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of Computational Physics , volume=

Reference 93

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source=arxiv_source observed=2026-08-02T02:48:55.841347Z digest=sha256:b0ce7937f7906b9e4ab972b44446f0ef5f8fc11f53cd95c5adb209d7308c1c13

Observation d4d40e78-1de6-47c8-8d7b-09726a657a62 · outbound

This paper cites Neurocomputing , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Neurocomputing , volume=

Reference 94

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source=arxiv_source observed=2026-08-02T02:48:55.907758Z digest=sha256:7b2dfe850905cdd7032817643f6398747e8255c86936e052bb4474bd01dcd891

Observation ff775d8c-e69b-4514-9ae8-1eba26a84490 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Computer Methods in Applied Mechanics and Engineering , volume=

Reference 95

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source=arxiv_source observed=2026-08-02T02:48:55.976004Z digest=sha256:f9ecff2e842eb809cbe0ad4f3a57cc6166d4a17984d578b47c89d2f9723c2be1

Observation 730032a4-ba79-4a1f-8762-35cb9a38bea1 · outbound

This paper cites Journal of Computational Physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of Computational Physics , volume=

Reference 96

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source=arxiv_source observed=2026-08-02T02:48:56.028895Z digest=sha256:dca6d64f0b9afef241ab393a98da3f261f57cf6dc9868f5fa24d9ab5ec18e0e1

Observation a7127a57-536b-4672-8646-0b161ca63077 · outbound

This paper cites Journal of Scientific Computing , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Journal of Scientific Computing , volume=

Reference 97

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source=arxiv_source observed=2026-08-02T02:48:56.102840Z digest=sha256:2491499a8a7625953cdf363ff8ec2f9ffd913f73644741a5d5cd0f6b6bd034fa

Observation 6d841d1e-8252-43e5-8054-c334b8be9591 · outbound

This paper cites Ihlenburg and I.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Ihlenburg and I

Reference 98

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source=arxiv_source observed=2026-08-02T02:48:56.168000Z digest=sha256:5c8b364a300b0276df5725ec4212f3babaa280e7f30a815ee5e6adf1dabd8de3

Observation 22b31231-b9fb-4470-96bb-4548a3e274a8 · outbound

This paper cites 2006 , publisher=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks 2006 , publisher=

Reference 99

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source=arxiv_source observed=2026-08-02T02:48:56.200983Z digest=sha256:853432e28da84f88f76bef82eddaf7a9a8eca335df6d42a3edee55c1c95d0732

Observation b43da72d-9d63-4a27-83bd-43ad80e32c31 · outbound

This paper cites Advances in neural information processing systems , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Advances in neural information processing systems , volume=

Reference 100

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source=arxiv_source observed=2026-08-02T02:48:56.268044Z digest=sha256:f246d2412a5d24713194371caf183dfeff7153b8fd130286f65e854cb06a731a

Observation 87195938-b215-4c35-aaba-f621abe26517 · outbound

This paper cites Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm

Reference 101

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source=arxiv_source observed=2026-08-02T02:48:56.333752Z digest=sha256:49eac0bd8cc4d88f3c69c0f2dc09db8fcd93553cec942cdcbd7ee3e5c8ee7dc8

Observation 8c74a724-75e5-4195-91e4-2ce3e8a136e4 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks On First-Order Meta-Learning Algorithms

Reference 102

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source=arxiv_source observed=2026-08-02T02:48:56.392764Z digest=sha256:54748659bc4fcf052851e17c30a1cbbcf91d780a1bfba644cc549f4ee14fcb48

Observation 55068356-05df-4b73-b50a-477022e11e58 · outbound

This paper cites Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=

Reference 103

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source=arxiv_source observed=2026-08-02T02:48:56.459893Z digest=sha256:ab2290eb7d2263e2d0fd79b1f9039e5142c3fd669659599d29132b1b7718c199

Observation 55a5933d-396d-4597-a6c0-112ac72fe4d9 · outbound

This paper cites Advances in neural information processing systems , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Advances in neural information processing systems , volume=

Reference 104

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source=arxiv_source observed=2026-08-02T02:48:56.530746Z digest=sha256:d12b50eb9a45ee559ad78a459057c7244a2c3c6a97b731a814b23fe4295195aa

Observation 4b3e9edf-9818-4d9d-8dfb-717c9f801acc · outbound

This paper cites Conference on learning theory , pages=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Conference on learning theory , pages=

Reference 105

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source=arxiv_source observed=2026-08-02T02:48:56.598683Z digest=sha256:f279985d8b2c93d1719ae0e72d21a56bd18b4dfc881c971af8415d233c0c243d

Observation 9b9a9e38-0e38-4c2c-b320-d3f9da8a1a17 · outbound

This paper cites Representation Benefits of Deep Feedforward Networks.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Representation Benefits of Deep Feedforward Networks

Reference 106

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source=arxiv_source observed=2026-08-02T02:48:56.663253Z digest=sha256:cf57ad27b1558e4a5362c1c9e95cf5c4ad007dfeab9867ffb02ae14c07b9a634

Observation eb3de6bd-c9be-4a6d-9de0-7ce0e67742c0 · outbound

This paper cites Theoretical and Applied Mechanics Letters , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Theoretical and Applied Mechanics Letters , volume=

Reference 107

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source=arxiv_source observed=2026-08-02T02:48:56.742465Z digest=sha256:9bc0b1f014ceeeb553989897c9c9a1ec414842b484da84df6fea51973234a032

Observation d6c3c792-e3b2-4589-9e02-a28560becbfb · outbound

This paper cites Computational Mechanics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Computational Mechanics , volume=

Reference 108

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source=arxiv_source observed=2026-08-02T02:48:56.799453Z digest=sha256:e5549a46604d08f7359c5e77fe69c9a99eaa3a34951d2ab4fa20a7123ad1f82a

Observation a818069f-a910-4290-97b2-4277ee7a88ef · outbound

This paper cites Frontiers in Physics , volume=.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Frontiers in Physics , volume=

Reference 109

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source=arxiv_source observed=2026-08-02T02:48:56.887214Z digest=sha256:5258f93a83342a7b601a3b17b472fdac49f95a21ef2e32cd3081374b31f96d8d

Pith citing papers

No inbound Pith citation observations are available.