Paper Citation Record · LEDGER

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks

As of 14 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.20851.

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

pith.paper-citation-record.v1
2412.20851 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:15:24.652258Z

measured 28 of 28 standing notices

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation 69ec0b5c-db57-497d-9eff-5cf543a67a29 · outbound

This paper cites Highly accurate protein structure prediction with AlphaFold,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Highly accurate protein structure prediction with AlphaFold,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.283555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b2da61c-9a2f-4f02-94b5-b57170c4f06c · outbound

This paper cites A Foundation Model for the Earth System.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks A Foundation Model for the Earth System

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.558382Z

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Observation 0debb26b-935a-4f8a-ab4c-fddf8078fe5e · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.272544Z

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Observation 5f2d68ff-781d-45f6-b7f1-9fcd7d9517ae · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Respecting causality is all you need for training physics-informed neural networks

Reference 4

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Observation 7cf657e4-21ab-4a53-9946-ddbfb1c63913 · outbound

This paper cites A Modified Physics Informed Neural Networks for Solving the Partial Differential Equation with Conservation Laws,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks A Modified Physics Informed Neural Networks for Solving the Partial Differential Equation with Conservation Laws,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.262390Z

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Observation 77432d22-d2c5-47ff-a6c1-d1da261549af · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.249137Z

Source-reported events for the cited work

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Observation 141fd3bd-32c7-465a-b65c-b0ae9441780c · outbound

This paper cites A General Neural- Networks-Based Method for Identification of Partial Differential Equations, Implemented on a Novel AI Accelerator,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks A General Neural- Networks-Based Method for Identification of Partial Differential Equations, Implemented on a Novel AI Accelerator,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.225895Z

Source-reported events for the cited work

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Observation 7b3c8742-a505-4542-95c3-87a66aa89953 · outbound

This paper cites Spectral Neural Operators.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Spectral Neural Operators

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.585359Z

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Observation 2523bde2-4459-4d0f-a8dd-28a34df2ddc6 · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.589617Z

Source-reported events for the cited work

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Observation fbb9fc17-fa66-49b0-bbb6-529a7f7000ba · outbound

This paper cites About optimal loss function for training physics-informed neural networks under respecting causality.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks About optimal loss function for training physics-informed neural networks under respecting causality

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:15:25.133004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 63a3a8d2-cf64-4de0-91d7-133f68d7146c · outbound

This paper cites hp-VPINNs: Variational physics-informed neural networks with domain decomposition,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks hp-VPINNs: Variational physics-informed neural networks with domain decomposition,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.597164Z

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Observation b1fa1529-1f85-4842-b7a0-f530a6653cac · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.600682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d46e3baf-fcbd-41cf-85c0-3fda30da131f · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.604249Z

Source-reported events for the cited work

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Observation e449bc90-498f-4f9e-9fa7-98aff77042b8 · outbound

This paper cites Thermodynamically consistent physics-informed neural networks for hyperbolic systems,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Thermodynamically consistent physics-informed neural networks for hyperbolic systems,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.607801Z

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Unavailable: canonical work link unavailable.

Observation cc87e8e4-559d-4dec-bdb8-b38b98b83e12 · outbound

This paper cites Physics-informed neural networks (PINNs) for fluid mechanics: a review,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Physics-informed neural networks (PINNs) for fluid mechanics: a review,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.610948Z

Source-reported events for the cited work

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Observation 1cadeff6-fab3-479b-9c00-550092af0c15 · outbound

This paper cites Solving the wave equation with physics-informed deep learning.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Solving the wave equation with physics-informed deep learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.614166Z

Source-reported events for the cited work

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Observation f3d2aebb-712a-47f2-aeb1-eca7818a748c · outbound

This paper cites A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.617712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b57fce-b1da-47e8-9a0e-ead51d4ea919 · outbound

This paper cites AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.620941Z

Source-reported events for the cited work

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Observation 7cbb1ef5-fa1a-4d42-9549-5216ceac37d3 · outbound

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

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.624240Z

Source-reported events for the cited work

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Observation 9fa66841-e631-41ac-8e78-af0a7a2a4c6f · outbound

This paper cites Element-wise multiplication based deeper physics-informed neural networks,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Element-wise multiplication based deeper physics-informed neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.214115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6b238d38-48d2-4072-9b14-c7740ca96684 · outbound

This paper cites Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks?

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:15:24.728068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8b819edc-43df-4152-ad12-d41fa21e4c8b · outbound

This paper cites Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.638603Z

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Unavailable: canonical work link unavailable.

Observation c1172fa7-91cf-448d-9a4a-092591b30303 · outbound

This paper cites Separable Physics-Informed Neural Networks for the solution of elasticity problems.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Separable Physics-Informed Neural Networks for the solution of elasticity problems

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:15:24.706969Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4e4a99d5-8f29-438e-8715-afc806f4c24e · outbound

This paper cites Paszke, S.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Paszke, S

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.202057Z

Source-reported events for the cited work

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Observation 0897150a-d803-4af1-8123-a33ec44ea704 · outbound

This paper cites Relativistic slingshot: A source for single circularly polarized attosecond x-ray pulses,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Relativistic slingshot: A source for single circularly polarized attosecond x-ray pulses,

Reference 25

Resolution: verified exact; doi, observed 2026-08-10T23:15:24.684115Z

Source-reported events for the cited work

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Observation d5758e77-fb7b-4219-8651-143e89e32609 · outbound

This paper cites Deterministic nonperiodic flow,.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Deterministic nonperiodic flow,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:15:25.188890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c0e739bf-8873-4594-b9d4-b3e6cf02a234 · outbound

This paper cites Available: https://doi.org/10.1038%2Fs42256-021-00302-5.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Available: https://doi.org/10.1038%2Fs42256-021-00302-5

Reference 2021

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T23:15:25.237265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e05fc044-3023-4a37-ac73-84d2d679d7a6 · outbound

This paper cites Element-wise Multiplication Based Deeper Physics-Informed Neural Networks.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks Element-wise Multiplication Based Deeper Physics-Informed Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:24.631347Z

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