Paper Citation Record · LEDGER

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.01714.

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

pith.paper-citation-record.v1
2507.01714 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:49:36.822802Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact3
verified fuzzy15
unresolved13
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 47c52f3e-4945-443b-aa56-01af16fa7aa0 · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:33.971925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a14ae595-9728-40c6-af9e-e993ec009892 · outbound

This paper cites On the role of fixed points of dynamical systems in training physics-informed neural networks,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling On the role of fixed points of dynamical systems in training physics-informed neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.915026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c5a84be-90e5-4b7a-84dc-2089c0084e2b · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Mitigating propagation failures in physics-informed neural networks using retain- resample-release (R3) sampling,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.803353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dcc1ec69-d411-4d57-95e0-f6928788866d · outbound

This paper cites Improved training of physics-informed neural networks with model ensembles,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Improved training of physics-informed neural networks with model ensembles,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:34.181364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df9aabaa-9861-4f35-afbd-2b5d54518967 · outbound

This paper cites MCMC using Hamiltonian dynamics,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling MCMC using Hamiltonian dynamics,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.651938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cf5e8d7d-6356-4003-ade7-fd902356aab2 · outbound

This paper cites The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo,

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:49:38.367583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1672b9bc-e6a3-4897-905e-0bf630d32212 · outbound

This paper cites Griewank and A.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Griewank and A

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:34.403266Z

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Observation 35b14941-d807-4a58-bfc1-ad81b9acf3d9 · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Understanding and mitigating gradient flow pathologies in physics-informed neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.505307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa808e6a-6b88-41a4-81a4-bd76731ebf81 · outbound

This paper cites Self-adaptive loss balanced physics-informed neural networks,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Self-adaptive loss balanced physics-informed neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.350874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a71bbf49-b476-407d-b9b8-eeae7a2c395b · outbound

This paper cites Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:49:38.135871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1bc15a2-b287-44f4-ad8c-887542bc41d3 · outbound

This paper cites Physics-informed neural networks with hard constraints for inverse design,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Physics-informed neural networks with hard constraints for inverse design,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:40.125795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e65edfd3-0fd4-47ef-aa9f-93546eaf22f1 · outbound

This paper cites A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks,

Reference 12

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:49:37.939560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e1d1e48-665c-4d3b-bfa8-c3ac449e6d09 · outbound

This paper cites How to Avoid Trivial Solutions in Physics-Informed Neural Networks.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling How to Avoid Trivial Solutions in Physics-Informed Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:34.959801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 316e2bf4-4a4a-4f3c-999a-5a2b284b6362 · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Learning in sinusoidal spaces with physics-informed neural networks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:35.062368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2bf4e84-4474-43d6-ad37-428b6e131c25 · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Characterizing possible failure modes in physics-informed neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.937411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e2c83542-0bb7-43b6-8ec2-3154902df709 · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Respecting causality for training physics-informed neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.768533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ded6300c-c435-4fa4-9289-51a925e1f4cb · outbound

This paper cites Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.591017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cdecd18a-f5a9-4e83-bdf8-20b089805bf5 · outbound

This paper cites A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.415790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c760541-3443-49a1-87ea-9dfb77f5da48 · outbound

This paper cites PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:49:37.619307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b0edce8-c4a9-4e9f-a65d-86ea16f4099b · outbound

This paper cites B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.267773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 78ffdbdb-c453-4b47-aa43-befd4074b691 · outbound

This paper cites Prior choice affects ability of Bayesian neural networks to identify unknowns.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Prior choice affects ability of Bayesian neural networks to identify unknowns

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:49:37.390671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 989bb760-9e3e-472e-b1c8-2d382f4594c8 · outbound

This paper cites Available: https://doi.org/10.1016/j.jcp.2020.109913.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Available: https://doi.org/10.1016/j.jcp.2020.109913

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:35.776215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 045219fb-e683-4cbf-be74-56f500237f5a · outbound

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

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling On the limited memory BFGS method for large scale optimization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:39.115195Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de74486e-39a2-4bf5-b211-9871006a6c41 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Adam: A Method for Stochastic Optimization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:35.927698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d12a49c2-c046-4fc4-826a-a82f9f282025 · outbound

This paper cites Challenges in training PINNs: A loss landscape perspective,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Challenges in training PINNs: A loss landscape perspective,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.952222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ceaeaf8c-658b-430c-8075-c7f2cab76b20 · outbound

This paper cites An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:49:37.127800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a200f880-9059-496e-aad6-7f7734ce7988 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Optuna: A next-generation hyperparameter optimization framework,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:36.205395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22c4bd60-b01b-46b9-8c77-f71003527980 · outbound

This paper cites TensorFlow Distributions.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling TensorFlow Distributions

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:36.537647Z

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Unavailable: canonical work link unavailable.

Observation 6298e1aa-29d5-4ddf-99db-a4b071034faf · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Pytorch: An imperative style, high-performance deep learning library,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.808603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5a496883-6478-43cc-aee8-f5851f1dce17 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:36.442236Z

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Unavailable: canonical work link unavailable.

Observation 0b3b1346-474d-4a1a-8af3-75b64c81a4f7 · outbound

This paper cites On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:36.822802Z

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Unavailable: canonical work link unavailable.

Observation 6b3a5d88-d2ee-45c4-98bf-bdf32e1ddce3 · outbound

This paper cites The relevance of Bayesian layer positioning to model uncertainty in deep Bayesian active learning,.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling The relevance of Bayesian layer positioning to model uncertainty in deep Bayesian active learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:49:38.634681Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cc5c00aa-a910-4225-95d7-a416909d8123 · outbound

This paper cites Available: https://doi.org/10.1007/BF01589116.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Available: https://doi.org/10.1007/BF01589116

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:36.080804Z

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Unavailable: canonical work link unavailable.

Observation 6b9e718e-45d9-46e8-a0f0-a6231e074cba · outbound

This paper cites Available: https://doi.org/10.1137/20M1318043.

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling Available: https://doi.org/10.1137/20M1318043

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:34.559712Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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