Paper Citation Record · LEDGER

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2504.12503.

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

pith.paper-citation-record.v1
2504.12503 v1

Coverage vector

measured 44 of 44 reference resolution

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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

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Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation d5c2b62f-f6ad-48e6-bff4-f7d53800360b · outbound

This paper cites A review of the artifi- cial neural network surrogate modeling in aerodynamic design,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study A review of the artifi- cial neural network surrogate modeling in aerodynamic design,

Reference 1

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This paper cites Drivaer- net: A parametric car dataset for data-driven aerody- namic design and prediction,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Drivaer- net: A parametric car dataset for data-driven aerody- namic design and prediction,

Reference 2

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This paper cites Surrogate modeling of car drag coefficient with depth and normal renderings,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Surrogate modeling of car drag coefficient with depth and normal renderings,

Reference 3

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This paper cites Fast predictions of aircraft aerodynamics using deep- learning techniques,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Fast predictions of aircraft aerodynamics using deep- learning techniques,

Reference 4

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This paper cites Data-driven design for metamaterials and multiscale systems: a review,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Data-driven design for metamaterials and multiscale systems: a review,

Reference 5

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Observation 0d29d190-357e-45e7-9c3f-41f4bbd46a05 · outbound

This paper cites Digital twins: state-of-the-art and future direc- tions for modeling and simulation in engineering dy- namics applications,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Digital twins: state-of-the-art and future direc- tions for modeling and simulation in engineering dy- namics applications,

Reference 6

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This paper cites Industrial applications of digital twins,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Industrial applications of digital twins,

Reference 7

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Observation 9bf48d16-01a2-44b0-b252-1b2fb2b353c4 · outbound

This paper cites Continual Learning: Applications and the Road Forward.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Continual Learning: Applications and the Road Forward

Reference 8

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This paper cites A comprehensive study of class incremental learning algorithms for visual tasks,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study A comprehensive study of class incremental learning algorithms for visual tasks,

Reference 9

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This paper cites Class- incremental learning: survey and performance eval- uation on image classification,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Class- incremental learning: survey and performance eval- uation on image classification,

Reference 10

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Observation 27e0287d-2759-4cc3-8eb2-bc1a896406b2 · outbound

This paper cites Gradient-based learning applied to document recogni- tion,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Gradient-based learning applied to document recogni- tion,

Reference 11

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This paper cites Core50: a new dataset and benchmark for continuous object recogni- tion,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Core50: a new dataset and benchmark for continuous object recogni- tion,

Reference 12

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This paper cites Clear: An adaptive contin- ual learning framework for regression tasks,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Clear: An adaptive contin- ual learning framework for regression tasks,

Reference 13

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This paper cites Three types of incremental learning,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Three types of incremental learning,

Reference 14

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This paper cites Overcoming catastrophic forgetting in neural net- works,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Overcoming catastrophic forgetting in neural net- works,

Reference 15

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Observation 145b4b2d-2bd8-4388-8ea8-aa6b6083cdaa · outbound

This paper cites P., and Wayne, G., 2019, Experience replay for continual learning.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study P., and Wayne, G., 2019, Experience replay for continual learning

Reference 16

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Unresolved cited work

Reference 17

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This paper cites A comprehensive survey of continual learning: Theory, method and application,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study A comprehensive survey of continual learning: Theory, method and application,

Reference 18

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Observation 3eae752e-e24e-4825-9141-17081604e2c9 · outbound

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study A continual learning survey: Defying forgetting in classification tasks,

Reference 19

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Observation 1b73288c-8163-44d9-a02d-dafe9a002352 · outbound

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Embracing change: Continual learning in deep neural networks,

Reference 20

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Observation 4a25b7a0-8d65-4b43-a2b7-bf17693c28e9 · outbound

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Online continual learning in image classification: An empirical survey,

Reference 21

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Continual Learning Should Move Beyond Incremental Classification

Reference 22

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study The clear benchmark: Continual learning on real- world imagery,

Reference 23

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Remind your neural network to pre- vent catastrophic forgetting,

Reference 24

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Observation 5044c3be-758a-44c5-9d80-796050d915a5 · outbound

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Online continual learning with maximal interfered re- trieval,

Reference 25

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This paper cites Dark experience for general con- 17 Copyright © by ASME tinual learning: a strong, simple baseline,.

Reference 26

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Con- tinual learning through synaptic intelligence,

Reference 27

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Memory aware synapses: Learning what (not) to forget,

Reference 28

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Learning without forget- ting,

Reference 29

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This paper cites icarl: Incremental classifier and repre- sentation learning,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study icarl: Incremental classifier and repre- sentation learning,

Reference 30

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This paper cites Op- timal continual learning has perfect memory and is np- hard,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Op- timal continual learning has perfect memory and is np- hard,

Reference 31

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This paper cites Continual learning for neural regression networks to cope with concept drift in industrial processes using convex optimisation,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Continual learning for neural regression networks to cope with concept drift in industrial processes using convex optimisation,

Reference 32

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Observation 6a847d83-dca4-411c-b3d9-5d524c59851a · outbound

This paper cites MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study MIRACLE3D: Memory-efficient Integrated Robust Approach for Continual Learning on Point Clouds via Shape Model Construction

Reference 33

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Continual learning in 3d point clouds: Employ- ing spectral techniques for exemplar selection,

Reference 34

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This paper cites On the use of surrogate models in engi- neering design optimization and exploration: The key issues,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study On the use of surrogate models in engi- neering design optimization and exploration: The key issues,

Reference 35

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This paper cites Practitioners guide to mlops: A framework for con- tinuous delivery and automation of machine learning,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Practitioners guide to mlops: A framework for con- tinuous delivery and automation of machine learning,

Reference 36

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This paper cites Ship-d: Ship hull dataset for design optimization using machine learning,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Ship-d: Ship hull dataset for design optimization using machine learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:34:34.005204Z

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Unresolved cited work

Reference 38

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This paper cites Drivaernet++: A large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Drivaernet++: A large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:34:33.983571Z

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Unresolved cited work

Reference 40

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Pointnet: Deep learning on point sets for 3d classi- fication and segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:34:33.958753Z

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Unresolved cited work

Reference 42

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Observation 3bc3c37a-ab56-46c7-8ac1-8211e3e08d59 · outbound

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Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Deep residual learning for image recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:34:33.936499Z

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Observation bcb97f69-f888-4e1c-9a9b-a4fe7d7a3c05 · outbound

This paper cites Avalanche: an end-to-end library for continual learn- ing,.

Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study Avalanche: an end-to-end library for continual learn- ing,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:34:33.925331Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.