Paper Citation Record · LEDGER

Advancing Generalization in PINNs through Latent-Space Representations

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2411.19125.

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

pith.paper-citation-record.v1
2411.19125 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:37:31.772575Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T05:42:41.806371Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:11:20.661561Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
verified fuzzy28
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation edb9486f-1ddc-4cf3-ac5a-30887ed8b1a5 · outbound

This paper cites The deep ritz method: a deep learning-based numerical algo- rithm for solving variational problems,.

Advancing Generalization in PINNs through Latent-Space Representations The deep ritz method: a deep learning-based numerical algo- rithm for solving variational problems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.320870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bcbb5d1-32fa-456a-81ba-1d3f3ad22bcc · outbound

This paper cites Neural Operator: Learning Maps Between Function Spaces.

Advancing Generalization in PINNs through Latent-Space Representations Neural Operator: Learning Maps Between Function Spaces

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.595120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a11b45dc-f6ea-4c4e-8ca1-1b70fa3572da · outbound

This paper cites Message passing neural pde solvers,.

Advancing Generalization in PINNs through Latent-Space Representations Message passing neural pde solvers,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.309000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21875402-e754-45ef-bf98-3ad40e0b70e0 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Advancing Generalization in PINNs through Latent-Space Representations Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.603878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14ac5534-7c1a-442e-ba1b-a516575a8c56 · outbound

This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,.

Advancing Generalization in PINNs through Latent-Space Representations Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.609058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f2db8f-3ab3-479c-8b50-d29867d31f2e · outbound

This paper cites Uncertainty quantification in estimating blood alcohol concentration from transdermal alcohol level with physics-informed neural networks,.

Advancing Generalization in PINNs through Latent-Space Representations Uncertainty quantification in estimating blood alcohol concentration from transdermal alcohol level with physics-informed neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.281982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2f88d74c-6ad3-4f63-b72f-f79607e8c72f · outbound

This paper cites Physics-informed neural networks with weighted losses by uncertainty evaluation for accurate and stable prediction of manufacturing systems,.

Advancing Generalization in PINNs through Latent-Space Representations Physics-informed neural networks with weighted losses by uncertainty evaluation for accurate and stable prediction of manufacturing systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.621263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ea7e203-23e5-4436-9535-87e592573139 · outbound

This paper cites Inherently interpretable physics-informed neural network for battery modeling and prognosis,.

Advancing Generalization in PINNs through Latent-Space Representations Inherently interpretable physics-informed neural network for battery modeling and prognosis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.262419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ec47377-fe75-45a9-bde6-8b6ed7c62a81 · outbound

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

Advancing Generalization in PINNs through Latent-Space Representations Fourier neural operator for parametric partial differential equations,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.250351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1f0c2fbf-1480-4a80-bfa9-52cf5d71143d · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,.

Advancing Generalization in PINNs through Latent-Space Representations Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.634958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c0ee339-39d7-440f-a31d-a38e887cbdc7 · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets,.

Advancing Generalization in PINNs through Latent-Space Representations Learning the solution operator of parametric partial differential equations with physics-informed deeponets,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.230087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7aec8ae6-d7da-4179-bde9-6d87decabd5a · outbound

This paper cites Deepsdf: Learning continuous signed distance functions for shape representation,.

Advancing Generalization in PINNs through Latent-Space Representations Deepsdf: Learning continuous signed distance functions for shape representation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.644679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a053e1c5-925e-4518-b5c8-44dba48100c8 · outbound

This paper cites Con- tinuous pde dynamics forecasting with implicit neural representations,.

Advancing Generalization in PINNs through Latent-Space Representations Con- tinuous pde dynamics forecasting with implicit neural representations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.209465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e18e89b-207b-4670-b20e-fd151eb58e75 · outbound

This paper cites Evolve smoothly, fit consistently: Learning smooth latent dynamics for advection-dominated systems,.

Advancing Generalization in PINNs through Latent-Space Representations Evolve smoothly, fit consistently: Learning smooth latent dynamics for advection-dominated systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.196656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73edc780-c9e4-4441-af6a-74e163430792 · outbound

This paper cites Neu- ral ordinary differential equations,.

Advancing Generalization in PINNs through Latent-Space Representations Neu- ral ordinary differential equations,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.184184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c571f802-d7c3-4a22-8081-af4448a84f19 · outbound

This paper cites Characterizing possible failure modes in physics-informed neural net- works,.

Advancing Generalization in PINNs through Latent-Space Representations Characterizing possible failure modes in physics-informed neural net- works,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.170739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 720cef04-eedf-4d10-878c-321f98d9f57d · outbound

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

Advancing Generalization in PINNs through Latent-Space Representations Understanding and mitigating gradient flow pathologies in physics-informed neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.157362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ef903407-9a8f-40dc-a449-263f93b6e4fe · outbound

This paper cites Meta-auto-decoder for solving parametric partial differential equations,.

Advancing Generalization in PINNs through Latent-Space Representations Meta-auto-decoder for solving parametric partial differential equations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.142935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 394ecaee-63b6-472d-80ae-6ff428ec8f42 · outbound

This paper cites Physics-informed neural ode (pinode): embedding physics into models using collocation points,.

Advancing Generalization in PINNs through Latent-Space Representations Physics-informed neural ode (pinode): embedding physics into models using collocation points,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.129869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a7e8f056-f4a7-4f8f-8c1d-8ed1502beef5 · outbound

This paper cites Implicit neural representations with periodic activation functions,.

Advancing Generalization in PINNs through Latent-Space Representations Implicit neural representations with periodic activation functions,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.114282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b39d3af6-fbe4-4f07-a1be-cafd10739ebd · outbound

This paper cites Multiplicative filter networks,.

Advancing Generalization in PINNs through Latent-Space Representations Multiplicative filter networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.099364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 091d8884-3634-4a1d-9a52-3be099d558d7 · outbound

This paper cites A high-efficient hybrid physics-informed neural networks based on convolutional neural network,.

Advancing Generalization in PINNs through Latent-Space Representations A high-efficient hybrid physics-informed neural networks based on convolutional neural network,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.683733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebd7848d-4f6b-429e-b322-1aea71ee4dea · outbound

This paper cites Rigorous a posteriori error bounds for pde-defined pinns,.

Advancing Generalization in PINNs through Latent-Space Representations Rigorous a posteriori error bounds for pde-defined pinns,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.078047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43ffa661-be51-4cf4-ba9f-e63eb6848824 · outbound

This paper cites Physics-informed neural networks for solving forward and inverse problems in complex beam systems,.

Advancing Generalization in PINNs through Latent-Space Representations Physics-informed neural networks for solving forward and inverse problems in complex beam systems,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.691835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56350972-1bf5-4490-83fe-07b6f620018d · outbound

This paper cites Parallel solution of nonlinear projection equations in a multitask learning framework,.

Advancing Generalization in PINNs through Latent-Space Representations Parallel solution of nonlinear projection equations in a multitask learning framework,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.057011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98d403ed-afe4-48be-8ba8-318d0a5e835b · outbound

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

Advancing Generalization in PINNs through Latent-Space Representations When and why pinns fail to train: A neural tangent kernel perspective,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.044554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b8c42a1-6af7-4c5d-b0f4-c468d2fa90b5 · outbound

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

Advancing Generalization in PINNs through Latent-Space Representations Respecting causality is all you need for training physics-informed neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.703320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0557501f-d6bf-4f88-bcd5-c15353e5b4aa · outbound

This paper cites Multiadam: Parameter- wise scale-invariant optimizer for multiscale training of physics-informed neural networks,.

Advancing Generalization in PINNs through Latent-Space Representations Multiadam: Parameter- wise scale-invariant optimizer for multiscale training of physics-informed neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.032218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b64f10b4-e8b2-48e1-b403-15b5b42f2e5d · outbound

This paper cites Separable physics-informed neural networks,.

Advancing Generalization in PINNs through Latent-Space Representations Separable physics-informed neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.019178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3bf77a66-8645-45e2-9d0c-e19590387105 · outbound

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

Advancing Generalization in PINNs through Latent-Space Representations Multipole graph neural operator for parametric partial differential equations,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:32.006807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eb122d8b-cdda-4d8d-a01f-6a94c4358cf3 · outbound

This paper cites Nonlinear reconstruction for operator learning of pdes with discontinuities,.

Advancing Generalization in PINNs through Latent-Space Representations Nonlinear reconstruction for operator learning of pdes with discontinuities,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.992747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3043c23d-e6c2-44ea-86be-60a5a2472cfb · outbound

This paper cites Physics-Informed Neural Operator for Learning Partial Differential Equations.

Advancing Generalization in PINNs through Latent-Space Representations Physics-Informed Neural Operator for Learning Partial Differential Equations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.723343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f81bf267-9d73-4532-94a0-8aa28865d61c · outbound

This paper cites Learning to optimize multigrid pde solvers,.

Advancing Generalization in PINNs through Latent-Space Representations Learning to optimize multigrid pde solvers,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.980299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c961624-6fa2-46c1-98f4-8efdbd212e5b · outbound

This paper cites Snode: Spectral discretization of neural odes for system identification,.

Advancing Generalization in PINNs through Latent-Space Representations Snode: Spectral discretization of neural odes for system identification,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.966973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c01fcff2-4bbf-4bd5-b59d-ff9e42eda34f · outbound

This paper cites Learning Dynamical Systems from Partial Observations.

Advancing Generalization in PINNs through Latent-Space Representations Learning Dynamical Systems from Partial Observations

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.734890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdc07dec-349a-4ce7-8356-3de737c3b61f · outbound

This paper cites Operator Learning with Neural Fields: Tackling PDEs on General Geometries.

Advancing Generalization in PINNs through Latent-Space Representations Operator Learning with Neural Fields: Tackling PDEs on General Geometries

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.739459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecf32819-d5cd-4094-812c-7d661ebb4144 · outbound

This paper cites Reduced-order modeling for parameterized PDEs via implicit neural representations.

Advancing Generalization in PINNs through Latent-Space Representations Reduced-order modeling for parameterized PDEs via implicit neural representations

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.744450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 192abc76-9242-4953-b2b6-2f5bcd5d8146 · outbound

This paper cites Automatic differentiation in machine learning: a survey,.

Advancing Generalization in PINNs through Latent-Space Representations Automatic differentiation in machine learning: a survey,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.749605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93cff12-a311-4c88-92bc-322edccdb6c5 · outbound

This paper cites How to train your neural ode: the world of jacobian and kinetic regularization,.

Advancing Generalization in PINNs through Latent-Space Representations How to train your neural ode: the world of jacobian and kinetic regularization,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.946649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 189671a7-a30b-4aeb-9018-1ced5886397b · outbound

This paper cites Long-time integration of parametric evolution equations with physics-informed deeponets,.

Advancing Generalization in PINNs through Latent-Space Representations Long-time integration of parametric evolution equations with physics-informed deeponets,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.934459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f330bc83-a597-4728-b87b-81ceb2ca951f · outbound

This paper cites Solving inverse-pde problems with physics-aware neural networks,.

Advancing Generalization in PINNs through Latent-Space Representations Solving inverse-pde problems with physics-aware neural networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.922276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23867ed7-8b0f-4708-a45e-a6eb4a3bef16 · outbound

This paper cites Learning to Solve PDE-constrained Inverse Problems with Graph Networks.

Advancing Generalization in PINNs through Latent-Space Representations Learning to Solve PDE-constrained Inverse Problems with Graph Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.764865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c246978c-cf70-4fa8-9501-1c9595ed34f3 · outbound

This paper cites Grids-net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning,.

Advancing Generalization in PINNs through Latent-Space Representations Grids-net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:37:31.907709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da046551-938e-4153-b0fa-ff01739938e5 · outbound

This paper cites torchdiffeq,.

Advancing Generalization in PINNs through Latent-Space Representations torchdiffeq,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T10:37:31.772575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 400a2fc4-8427-4629-a0af-98117897179e · inbound

Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD cites this paper.

Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD Advancing Generalization in PINNs through Latent-Space Representations

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:11:20.662797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.