Paper Citation Record · LEDGER

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2504.15240.

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

pith.paper-citation-record.v1
2504.15240 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:32:21.332585Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:11:04.005789Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:11:04.054740Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy15
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c1de2ae8-5993-40e4-b7a5-9904f025d6dd · outbound

This paper cites Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.553277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 984956cb-30b0-44ac-9d68-e6063b27e5c6 · outbound

This paper cites Physics-informed machine learning.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Physics-informed machine learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.003713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c934c3b-6336-4a38-aa01-c3c666f97646 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning KAN: Kolmogorov-Arnold Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.011816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a827dd84-c2bc-4ca8-818d-47246af8d795 · outbound

This paper cites KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.017428Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c59c652e-f135-42e2-8618-fa9b7e412aac · outbound

This paper cites On the representations of continuous functions of many variables by superposition of continuous functions of one variable and addition.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning On the representations of continuous functions of many variables by superposition of continuous functions of one variable and addition

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.528463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46d80b4e-32eb-4fa5-82f7-f9a7aefb1a2f · outbound

This paper cites KAN or MLP: A Fairer Comparison.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning KAN or MLP: A Fairer Comparison

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.030347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 695551af-2ff8-4012-a34a-0ec7899174d5 · outbound

This paper cites KAN versus MLP on Irregular or Noisy Functions.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning KAN versus MLP on Irregular or Noisy Functions

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.038141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f52f2bd-3c30-4940-b2b9-3cd7c7dadb6a · outbound

This paper cites A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.045518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d5fdb73-1923-456b-a44c-d7b3fbe4e74e · outbound

This paper cites Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.052180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33a94de7-5e9c-4e77-8922-4b2e3856b0fc · outbound

This paper cites Adaptive training of grid-dependent physics-informed kolmogorov-arnold networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Adaptive training of grid-dependent physics-informed kolmogorov-arnold networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.512974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82b772ac-fb87-4db0-9788-65b0ea5ea2d0 · outbound

This paper cites Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.068101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 643a7563-e822-47d4-a6d5-7907ae54b727 · outbound

This paper cites TKAN: Temporal Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning TKAN: Temporal Kolmogorov-Arnold Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.073216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04470a80-436f-4bcc-87ed-61a7c8b3f1a7 · outbound

This paper cites Wav-KAN: Wavelet Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.079974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 943c4bb4-be9a-479d-b9aa-32e06510f5c9 · outbound

This paper cites Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.086920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3002a488-bf7b-4989-ad4a-f155ff6b5377 · outbound

This paper cites On the study of frequency control and spectral bias in Wavelet-Based Kolmogorov Arnold networks: A path to physics-informed KANs.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning On the study of frequency control and spectral bias in Wavelet-Based Kolmogorov Arnold networks: A path to physics-informed KANs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.091939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 462a2335-c762-4dba-9e8f-8bf29545b579 · outbound

This paper cites GKAN: Graph Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning GKAN: Graph Kolmogorov-Arnold Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.097809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 563db581-63b8-4b00-801a-f4fdc098744d · outbound

This paper cites GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.103459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec9bb94d-aa3a-4754-8e95-95362964fc38 · outbound

This paper cites Kolmogorov-Arnold Graph Neural Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Kolmogorov-Arnold Graph Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.109140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a0c4773-0f35-4e28-b78d-89c633076a94 · outbound

This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.115427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f21c2931-ed86-47bd-82d1-cc7244affdb8 · outbound

This paper cites Scaled-cPIKANs: Domain Scaling in Chebyshev-based Physics-informed Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Scaled-cPIKANs: Domain Scaling in Chebyshev-based Physics-informed Kolmogorov-Arnold Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.120868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8e2d33e-7da1-4324-8ca9-199550e0ea8e · outbound

This paper cites fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.130692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39bd6fa2-b823-48b7-bb8c-d419cf31d51e · outbound

This paper cites DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.138420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96525fa3-ea79-4003-8cd3-03cd45cc9cf0 · outbound

This paper cites Kolmogorov-arnold networks (kans) for time series analysis.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Kolmogorov-arnold networks (kans) for time series analysis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.148679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b850f5b-43cb-4226-9106-898e33c5f4e3 · outbound

This paper cites Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.161894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 324d1631-6513-4525-8b06-13ebcb9bd648 · outbound

This paper cites Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.166623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19bb2fbf-8969-47af-8e45-08e621f82056 · outbound

This paper cites Suitability of KANs for Computer Vision: A preliminary investigation.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Suitability of KANs for Computer Vision: A preliminary investigation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.172749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 046d466d-07a6-497f-b2ac-a74f30421130 · outbound

This paper cites Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.179381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 330a2e96-d001-4819-aedf-b1ffaafe76e0 · outbound

This paper cites Finite basis kolmogorov-arnold networks: domain decomposition for data-driven and physics-informed problems.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Finite basis kolmogorov-arnold networks: domain decomposition for data-driven and physics-informed problems

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.189422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06e22086-6857-4ebc-a01c-67ed8eab1faa · outbound

This paper cites Multifidelity Kolmogorov-Arnold Networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Multifidelity Kolmogorov-Arnold Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.195600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5a2209-8d41-4872-a433-5121959c6e85 · outbound

This paper cites Raissi, P.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Raissi, P

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.201843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70fe4c9b-66d7-4771-8df5-b1f7d2c6a4de · outbound

This paper cites Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.479744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c294d332-b8f5-48c1-b630-5cfd05733a17 · outbound

This paper cites Scalable uncertainty quantification for deep operator networks using randomized priors.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Scalable uncertainty quantification for deep operator networks using randomized priors

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.461102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2fac36be-dbe7-4ab9-988b-63f1ad388f09 · outbound

This paper cites B-deeponet: An enhanced bayesian deeponet for solving noisy parametric pdes using accelerated replica exchange sgld.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning B-deeponet: An enhanced bayesian deeponet for solving noisy parametric pdes using accelerated replica exchange sgld

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.445848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05e44bb2-2dec-4514-90bf-a9a2cc15d4e9 · outbound

This paper cites Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.428179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e51b4284-df84-4192-9b28-23c075e26c5c · outbound

This paper cites Bayesian deep operator learning for homogenized to fine-scale maps for multiscale pde.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Bayesian deep operator learning for homogenized to fine-scale maps for multiscale pde

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.413535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 23e38a55-0359-4b15-a387-5c61db5f919b · outbound

This paper cites Uncertainty quantification with bayesian higher order relu-kans.

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.396388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 049bf507-8602-4152-b085-0d72ebb8e632 · outbound

This paper cites Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.262820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005ebf06-3573-44d2-b6f0-710a6661878f · outbound

This paper cites Deep operator learning-based surrogate models with uncertainty quantification for optimizing internal cooling channel rib profiles.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Deep operator learning-based surrogate models with uncertainty quantification for optimizing internal cooling channel rib profiles

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.369469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7f03339d-3cc8-413f-89be-51dce46c7c91 · outbound

This paper cites Springer, 2005.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Springer, 2005

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.351678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 232b4aaf-4818-4df4-ab79-4049aa6ef9f3 · outbound

This paper cites Conformalized quantile regression.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Conformalized quantile regression

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.281160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3efde627-434e-48fc-98b4-5f059473de87 · outbound

This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.289121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce12d79-c2c9-4589-83ff-e67f160e2f72 · outbound

This paper cites Conformalized-deeponet: A distribution- free framework for uncertainty quantification in deep operator networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Conformalized-deeponet: A distribution- free framework for uncertainty quantification in deep operator networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.322650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5fe23355-287f-4fa4-a0a8-024ffc99d41b · outbound

This paper cites Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.301511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd91abe-2493-43c7-b74a-dc0c7b860083 · outbound

This paper cites Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Finite basis physics-informed neural networks (fbpinns): a scalable domain decomposition approach for solving differential equations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.306069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f1133d4-52cc-4e51-8c10-4aa04d7a1fe9 · outbound

This paper cites Multilevel domain decomposition-based ar- chitectures for physics-informed neural networks.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Multilevel domain decomposition-based ar- chitectures for physics-informed neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.286342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6ab9492e-41ba-4211-a399-2ec7a0333e8f · outbound

This paper cites ELM-FBPINNs: An Efficient Multilevel Random Feature Method.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning ELM-FBPINNs: An Efficient Multilevel Random Feature Method

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.322736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90e405a1-7b62-450c-bdd3-4adf18a08429 · outbound

This paper cites Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T11:32:21.327979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bd8000c-c939-46ee-8051-daa74226b016 · outbound

This paper cites A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems.

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:32:22.269894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 8fe07d64-d00a-4e4e-93d1-bd0e265e4d30 · inbound

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony cites this paper.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:11:04.060284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.