Paper Citation Record · LEDGER

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks

As of 22 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 4 inbound Pith citation observations for arXiv:2502.00803.

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

pith.paper-citation-record.v1
2502.00803 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:45:05.865790Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:59.932121Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T00:02:49.919697Z

Reference resolution

50 of 50 outbound references displayed

verified exact2
verified fuzzy38
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ab43dfc-6e7d-468e-a58c-cde9313bad00 · outbound

This paper cites Numerical methods for partial differential equations.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Numerical methods for partial differential equations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.682268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 523d2fd8-e67f-4ae5-83bf-d32e1ec43578 · outbound

This paper cites L-pinn: A langevin dynamics approach with balanced sampling to improve learning stability in physics-informed neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks L-pinn: A langevin dynamics approach with balanced sampling to improve learning stability in physics-informed neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.666549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 220deb87-bd01-42d1-a42c-3fc3b57b7b83 · outbound

This paper cites An elementary introduction to modern convex geometry.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks An elementary introduction to modern convex geometry

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.650874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77ebb102-e2a1-4504-98c8-f415a27b95df · outbound

This paper cites Optimal control and viscosity solutions of Hamilton-Jacobi- Bellman equations.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Optimal control and viscosity solutions of Hamilton-Jacobi- Bellman equations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.635300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8f0bced-16bc-4958-85ad-fcf45d8a2712 · outbound

This paper cites The Schrödinger Equation.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks The Schrödinger Equation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.619650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6968de88-60a7-48b7-a2cd-418ec3f65742 · outbound

This paper cites JAX: composable transformations of Python+NumPy programs, 2018.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks JAX: composable transformations of Python+NumPy programs, 2018

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.654880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce8b2a44-9b59-44f7-8c92-58b8e93af739 · outbound

This paper cites Quadratic residual networks: A new class of neural networks for solving forward and inverse problems in physics involving pdes.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Quadratic residual networks: A new class of neural networks for solving forward and inverse problems in physics involving pdes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.593462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9cf806b5-0377-4834-b655-824ae7697a68 · outbound

This paper cites University of Chicago press, 1988.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks University of Chicago press, 1988

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.577777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ed425203-8146-4b78-906a-16256f02a8d3 · outbound

This paper cites Mitigating propagation failures in physics-informed neural networks using retain-resample-release (R3) sampling.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Mitigating propagation failures in physics-informed neural networks using retain-resample-release (R3) sampling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.562012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 342cc9e1-9b3d-481f-8f73-728615db4b84 · outbound

This paper cites Finite element method.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Finite element method

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.545873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ff76d31-6fce-4d1a-8eee-92733fa9bea1 · outbound

This paper cites Applied analysis of the Navier-Stokes equations.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Applied analysis of the Navier-Stokes equations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.530620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b9492e6a-0759-444e-bc9b-f62c70693cf6 · outbound

This paper cites Partial differential equations.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Partial differential equations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.514566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ee7030e-8c38-4c5d-87ec-d563a4340e86 · outbound

This paper cites The jacobi method for real symmetric matrices.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks The jacobi method for real symmetric matrices

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.497651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1f2d2360-f864-464e-bdad-050337e3ca26 · outbound

This paper cites Domain decomposition for multiscale pdes.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Domain decomposition for multiscale pdes

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.480991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ee286188-19c6-460d-92b1-4b6800e41bb9 · outbound

This paper cites Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.696333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 445f19b5-8e70-42cd-960e-4860f1efb199 · outbound

This paper cites Tackling the curse of dimensionality with physics-informed neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Tackling the curse of dimensionality with physics-informed neural networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.465168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48fcad46-dde1-43cd-a48c-47e77e829dd8 · outbound

This paper cites Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:45:06.000419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8edb7dcc-9b81-4cbc-92d4-83765ad92fd9 · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Neural tangent kernel: Convergence and generalization in neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.449194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35f26c32-9291-4d67-b591-8b26270b4aa6 · outbound

This paper cites Spectral/hp element methods for computational fluid dynamics.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Spectral/hp element methods for computational fluid dynamics

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.432703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 72daa492-6d80-4be6-aada-466123bc146d · outbound

This paper cites Kingma and Jimmy Ba.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Kingma and Jimmy Ba

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.720021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 936b2f88-7027-4447-94e9-60611b40df53 · outbound

This paper cites Implementing spectral methods for partial differential equations: Algorithms for scientists and engineers.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Implementing spectral methods for partial differential equations: Algorithms for scientists and engineers

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.406882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ce36e0b9-f386-4066-8621-a710ae7b9745 · outbound

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

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Characterizing possible failure modes in physics-informed neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.391171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f435d27a-5565-4245-ba08-54c2c68ea852 · outbound

This paper cites On the limited memory bfgs method for large scale optimization.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks On the limited memory bfgs method for large scale optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.375496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0322bc3c-c32c-4bc2-8c66-18dea6c49660 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks KAN: Kolmogorov-Arnold Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.738019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 920c56d5-79b4-4391-9d39-4414c91bd451 · outbound

This paper cites Aerodynamics, aeronautics, and flight mechanics.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Aerodynamics, aeronautics, and flight mechanics

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.358581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5aa6c91-80f0-4ed2-aac9-e057fb314dbc · outbound

This paper cites SetPINNs: Set-based Physics-informed Neural Networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks SetPINNs: Set-based Physics-informed Neural Networks

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:45:05.959879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e643e0bd-b7f5-4d25-b029-43e9e4a2b791 · outbound

This paper cites Gross, Francisco Massa, A.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Gross, Francisco Massa, A

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.342826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a65fe2bb-9b77-4f59-9e5a-d59990222148 · outbound

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

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.326679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9a32f49-5f3b-4114-a414-7aa9dba61110 · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.761788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57c99a71-7e53-4253-bd97-ce1edca507f8 · outbound

This paper cites Nonlinear partial differential equations with applications.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Nonlinear partial differential equations with applications

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.310849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a693d311-7a27-48d7-98a3-addfcaea1aea · outbound

This paper cites Stochastic taylor derivative estimator: Efficient amortization for arbitrary differential operators.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Stochastic taylor derivative estimator: Efficient amortization for arbitrary differential operators

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.294381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93c0606b-3247-46c3-9b02-f375309088bb · outbound

This paper cites Partial differential equations and the finite element method.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Partial differential equations and the finite element method

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.776986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08b99555-2075-400e-a560-03a91dd3e93e · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Fourier features let networks learn high frequency functions in low dimensional domains

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.781598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9132487-b35a-4527-ab0d-dcc7bbbe3c29 · outbound

This paper cites Navier-Stokes equations: theory and numerical analysis.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Navier-Stokes equations: theory and numerical analysis

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.258643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0bb11b19-d889-4c21-baae-1e5c9a7568ab · outbound

This paper cites Attention is all you need.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Attention is all you need

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.791229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efb3cacc-22e2-4578-b1cd-455e78944daf · outbound

This paper cites Is l2 physics informed loss always suitable for training physics informed neural network? NeurIPS, 2022.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Is l2 physics informed loss always suitable for training physics informed neural network? NeurIPS, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.233045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1fb8d9cb-1f50-4697-8fcc-a3f03638e19d · outbound

This paper cites Scientific discovery in the age of artificial intelligence.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Scientific discovery in the age of artificial intelligence

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.217164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 11af5d6b-09ec-40a3-bbc3-ca641ff24f94 · outbound

This paper cites PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.805749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce9edc3a-c543-478d-b2ae-733798d5c322 · outbound

This paper cites Respecting causality for training physics-informed neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Respecting causality for training physics-informed neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.201438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09bff5a5-e712-44fd-a7b9-31ede48d9137 · outbound

This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks An Expert's Guide to Training Physics-informed Neural Networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T17:45:05.816499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4e68504-6859-42f7-b7de-dcc15b0a7db2 · outbound

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

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks When and why pinns fail to train: A neural tangent kernel perspective

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.185282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8121ba8-f5ea-44c7-bef1-bbf1b11aac69 · outbound

This paper cites Partial differential equations: methods and applications.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Partial differential equations: methods and applications

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.169245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 02ea6b38-d965-4cfa-ace0-6ebfc0e115cf · outbound

This paper cites Karman vortex streets.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Karman vortex streets

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.153432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb37fcdb-7958-45d3-9afd-fc3b591d00ea · outbound

This paper cites Learning in sinusoidal spaces with physics-informed neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Learning in sinusoidal spaces with physics-informed neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.137121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ac2f397-e1dc-4738-9252-0a25273f4a0b · outbound

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

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.119598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba73444a-9bba-48c0-a7fd-01420de66881 · outbound

This paper cites Ropinn: Region optimized physics-informed neural networks.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Ropinn: Region optimized physics-informed neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.101991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 60007841-8932-440c-bd09-3baa35386a5d · outbound

This paper cites Stiffness matrix for geometric nonlinear analysis.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Stiffness matrix for geometric nonlinear analysis

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.085319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9c592b9f-f973-4aa6-a44c-efcd5d25ecb6 · outbound

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

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks Gradient-enhanced physics-informed neural networks for forward and inverse pde problems

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.068689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e2f5e59f-8eb5-4744-b502-2d978f5d4051 · outbound

This paper cites overfitted.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks overfitted

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.052066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 912850da-7894-459d-9f63-52b35ffaeda9 · outbound

This paper cites representation correlation.

ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks representation correlation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:45:06.036098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 5903e98b-b358-4b94-84c2-77c62df59d86 · inbound

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:59.932121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a99250dc-590d-4181-85d2-780bcf29453e · inbound

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation cites this paper.

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T19:36:17.421647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5762360b-e776-4435-b044-02912028ada2 · inbound

PINNs Failure Modes are Overfitting cites this paper.

PINNs Failure Modes are Overfitting ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:24:24.689340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a3729af-bf90-41a8-92b8-97a1961cb3fb · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-31T02:27:16.891206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.