Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2201.05624.

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

pith.paper-citation-record.v1
2201.05624 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:49:25.206209Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

83
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6ef4ad1c-9a8e-4302-9f14-8e2598b4b8df · inbound

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics cites this paper.

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 271

Resolution: verified exact; arxiv_id, observed 2026-05-24T10:24:20.267742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d724c843-3aea-42aa-94cb-51b9368ff3db · inbound

Bayesian Reasoning for Physics Informed Neural Networks cites this paper.

Bayesian Reasoning for Physics Informed Neural Networks Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:06:03.931897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 78374db2-0ace-4c02-9b85-80408522c55b · inbound

Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment cites this paper.

Partial-differential-algebraic equations of nonlinear dynamics by Physics-Informed Neural-Network: (I) Operator splitting and framework assessment Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:58:34.405487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 269026b7-3ec7-4f55-bfef-2f9d2de53709 · inbound

Evaluation of Neural Surrogates for Physical Modelling Synthesis of Nonlinear Elastic Plates cites this paper.

Evaluation of Neural Surrogates for Physical Modelling Synthesis of Nonlinear Elastic Plates Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T16:49:25.206209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebf0427a-40bd-49bd-995e-caded4e6e6ef · inbound

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations cites this paper.

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T16:12:20.734391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 512202cc-55ef-4a49-bd57-8dd20448b845 · inbound

Physics-Informed Global Extraction of the Universal Small-$x$ Dipole Amplitude cites this paper.

Physics-Informed Global Extraction of the Universal Small-$x$ Dipole Amplitude Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T18:39:16.354663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1ad5208-4510-40ce-8a37-aab34f604cb3 · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:16:21.785386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b8ff2aa-90e0-4af1-ab71-d48042916d57 · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:18:04.101921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50857a9a-4e72-46c3-8595-aeabcb0c070f · inbound

Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment cites this paper.

Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:24:41.349358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a40fe82b-9b60-41c2-a5d8-cc44b379a5a1 · inbound

The physics of AI weather models cites this paper.

The physics of AI weather models Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-25T02:25:14.345174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f36968e0-21fd-41a6-bb3c-3ad2fc61992f · inbound

Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources cites this paper.

Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-27T09:50:48.591704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9b1c565-a611-4a9d-b92c-2ad71cfbb783 · inbound

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks cites this paper.

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T22:29:00.675484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b3668b3-8f50-4283-9a05-190a656614ff · inbound

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy cites this paper.

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T05:11:07.637142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-02T02:48:48.181668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 380f8b9d-aaa3-4fdb-b5d4-50dd405be8ed · inbound

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers cites this paper.

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T04:51:43.634659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65ae833b-63f1-48af-9034-396fcd84a0d9 · inbound

Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate cites this paper.

Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T04:59:44.694834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d87f790-afd4-48bc-9aef-ac6eefb64a87 · inbound

Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics cites this paper.

Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T16:56:02.464012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.