Paper Citation Record · LEDGER

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications

As of 24 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2411.18459.

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

pith.paper-citation-record.v1
2411.18459 v1

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measured 39 of 39 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-05T12:22:10.345975Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 57f86cfb-2d76-4fa7-98c9-5f0e2749c860 · outbound

This paper cites Integrating Machine Learning and Multiscale Modeling—Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Integrating Machine Learning and Multiscale Modeling—Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences

Reference 1

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Observation 1741d413-9f3d-4bd6-98c6-b8107af82b1f · outbound

This paper cites A Convergence Theory for Deep Learn- ing via Over-Parameterization.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications A Convergence Theory for Deep Learn- ing via Over-Parameterization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:14:22.726661Z

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Observation 1c78ad35-d755-4651-b8a2-a8901b0ad653 · outbound

This paper cites The influence of pattern similarity and transfer learning upon training of a base perceptron b2.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications The influence of pattern similarity and transfer learning upon training of a base perceptron b2

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:14:22.715507Z

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Observation 796104ba-183d-45d1-ac3c-6ff88d6b1c0f · outbound

This paper cites Universal Approximation to Nonlinear Operators by Neu- ral Networks with Arbitrary Activation Functions and Its Application to Dynamical Sys- tems.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Universal Approximation to Nonlinear Operators by Neu- ral Networks with Arbitrary Activation Functions and Its Application to Dynamical Sys- tems

Reference 4

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Observation b752a54f-8025-40bb-8e2f-399c70e6b4bc · outbound

This paper cites Supervised Learning of Universal Sentence Representations from Natural Language Inference Data.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Reference 5

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Observation 3092bbaa-67ee-46cf-9f58-34965bd6d0b9 · outbound

This paper cites Transfer Learning for Text Classification.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Transfer Learning for Text Classification

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:14:22.702783Z

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Observation 8672a5ad-d7ad-4eab-8e67-71032e7e0528 · outbound

This paper cites Gradient Descent Finds Global Minima of Deep Neural Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Gradient Descent Finds Global Minima of Deep Neural Networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:14:22.690561Z

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Observation 219bdda2-7925-4658-94da-a01c1aca45e1 · outbound

This paper cites Spectra of the Conjugate Kernel and Neural Tangent Kernel for Linear-Width Neural Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Spectra of the Conjugate Kernel and Neural Tangent Kernel for Linear-Width Neural Networks

Reference 8

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Observation a3b0e0e8-2be8-478c-bc65-10bf34297146 · outbound

This paper cites Understanding the Difficulty of Training Deep Feedfor- ward Neural Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Understanding the Difficulty of Training Deep Feedfor- ward Neural Networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:14:22.655295Z

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Observation 64443789-b8a7-4879-893a-0353fd556f84 · outbound

This paper cites Deep Learning.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Deep Learning

Reference 10

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Observation b84ec8e3-9a84-48a1-8d7e-dc27c7fb0249 · outbound

This paper cites Deep Transfer Operator Learning for Partial Differential Equa- tions under Conditional Shift.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Deep Transfer Operator Learning for Partial Differential Equa- tions under Conditional Shift

Reference 11

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Observation 4c715c16-47a0-4d77-980e-1a263b9650e8 · outbound

This paper cites The conjugate kernel for efficient training of physics-informed deep operator networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications The conjugate kernel for efficient training of physics-informed deep operator networks

Reference 12

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Observation bb88292d-a270-4a7c-8257-e98b9d8fc56e · outbound

This paper cites Stacked Networks Improve Physics-Informed Training: Appli- cations to Neural Networks and Deep Operator Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Stacked Networks Improve Physics-Informed Training: Appli- cations to Neural Networks and Deep Operator Networks

Reference 13

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Observation e9bc367e-b7da-40b7-8b54-442e00c4eb90 · outbound

This paper cites Operator Transfer Learning for Physics Field Prediction on Complex Geometries with Limited Labelled Data.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Operator Transfer Learning for Physics Field Prediction on Complex Geometries with Limited Labelled Data

Reference 14

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Observation 0b264293-ffc2-490f-821a-61d5c7a91fe3 · outbound

This paper cites A Review of Deep Transfer Learning and Recent Advancements.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications A Review of Deep Transfer Learning and Recent Advancements

Reference 15

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Observation 85f75363-a404-4d42-a9c7-39204b38e149 · outbound

This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Neural Tangent Kernel: Convergence and Generalization in Neural Networks

Reference 16

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Observation aa7513e7-5b11-495b-acd3-1b38dc7ba7d3 · outbound

This paper cites On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations

Reference 17

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Observation ae2864a5-2041-41a2-a303-5814729dd4ea · outbound

This paper cites Physics-Informed Machine Learning.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Physics-Informed Machine Learning

Reference 18

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Observation e40e5a63-b732-4a8e-8f83-091b31170c7d · outbound

This paper cites Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving

Reference 19

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Observation 7a53b4bf-da59-4865-ba67-fc70ca6532b9 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Fourier Neural Operator for Parametric Partial Differential Equations

Reference 20

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Observation 21481c17-e8c0-4331-aa44-6fdd0b0f569a · outbound

This paper cites Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Exploring Transfer Learning to Reduce Training Overhead of HPC Data in Machine Learning

Reference 21

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Observation 46ac893d-8627-45d5-a4cd-f7026bf9c539 · outbound

This paper cites Learning Nonlinear Operators via DeepONet Based on the Universal Approx- imation Theorem of Operators.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Learning Nonlinear Operators via DeepONet Based on the Universal Approx- imation Theorem of Operators

Reference 22

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Observation a7028694-68ba-442e-ac41-682127c749a9 · outbound

This paper cites Machine-Learning-Based Spectral Methods for Partial Differential Equations.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Machine-Learning-Based Spectral Methods for Partial Differential Equations

Reference 23

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Observation 3684b342-120c-44e4-9bae-ad36d4c907e4 · outbound

This paper cites Efficient kernel surrogates for neural network-based regression.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Efficient kernel surrogates for neural network-based regression

Reference 24

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Observation 1144a43c-f2b0-4e53-97be-1b915347d399 · outbound

This paper cites Deep Convolutional Neural Networks for Computer-Aided Detec- tion: CNN Architectures, Dataset Characteristics and Transfer Learning.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Deep Convolutional Neural Networks for Computer-Aided Detec- tion: CNN Architectures, Dataset Characteristics and Transfer Learning

Reference 25

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Observation 9a3b386c-0750-4038-ab2a-441c616c58ec · outbound

This paper cites Harnessing the Power of Transfer Learning in Deep Learning Models.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Harnessing the Power of Transfer Learning in Deep Learning Models

Reference 26

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Observation bfbf338c-f410-4ddd-a4ce-9e083a2e04a3 · outbound

This paper cites Long-Time Integration of Parametric Evolution Equations with Physics-Informed DeepONets.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Long-Time Integration of Parametric Evolution Equations with Physics-Informed DeepONets

Reference 27

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Observation 1b795f2d-cfe0-4f8e-bdf1-b1abfe1cea42 · outbound

This paper cites Respecting Causality for Training Physics- Informed Neural Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Respecting Causality for Training Physics- Informed Neural Networks

Reference 28

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Observation c069cdbe-2728-47e9-b94f-23c02e60c092 · outbound

This paper cites Improved Architectures and Training Algorithms for Deep Operator Networks.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Improved Architectures and Training Algorithms for Deep Operator Networks

Reference 29

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Observation f0f4d5a3-0969-4d24-96f0-097294450f12 · outbound

This paper cites Learning the Solution Operator of Para- metric Partial Differential Equations with Physics-Informed DeepONets.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Learning the Solution Operator of Para- metric Partial Differential Equations with Physics-Informed DeepONets

Reference 30

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Observation 0004d652-5cf3-41a1-9a8a-e13bab59da16 · outbound

This paper cites Fine-Tuning DeepONets to Enhance Physics-informed Neural Networks for solving Partial Differential Equations.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Fine-Tuning DeepONets to Enhance Physics-informed Neural Networks for solving Partial Differential Equations

Reference 31

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Observation ea4becd8-9286-4db4-a368-110c64bd9e5e · outbound

This paper cites A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construc- tion.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construc- tion

Reference 32

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Observation e3f35c53-dbf3-480a-bb78-4bfcf999c9f3 · outbound

This paper cites Transfer Learning Enhanced DeepONet for Long- Time Prediction of Evolution Equations.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Transfer Learning Enhanced DeepONet for Long- Time Prediction of Evolution Equations

Reference 33

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Observation 3cae3a9f-0008-4053-8609-3b643a6a395e · outbound

This paper cites How Transferable Are Features in Deep Neural Networks?.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications How Transferable Are Features in Deep Neural Networks?

Reference 34

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This paper cites DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning

Reference 35

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What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications Unresolved cited work

Reference 256

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This paper cites DOI: 10.1038/s42256-022-00569-2.

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications DOI: 10.1038/s42256-022-00569-2

Reference 1164

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This paper cites (Visited on 05/08/2024).

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications (Visited on 05/08/2024)

Reference 2322

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This paper cites (Visited on 11/06/2024).

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications (Visited on 11/06/2024)

Reference 7474

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Pith citing papers

Observation 998df493-0241-4503-b319-225bc7c1d977 · inbound

Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks cites this paper.

Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications

Reference 38

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