Paper Citation Record · LEDGER

Non-smooth optimization meets automated material model discovery

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 2 inbound Pith citation observations for arXiv:2507.10196.

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

pith.paper-citation-record.v1
2507.10196 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:42:59.211535Z

measured 63 of 63 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T05:38:21.760932Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:04:46.462847Z

Reference resolution

61 of 61 outbound references displayed

verified exact10
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier8
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 074029be-da20-49b6-915c-c02e75f6475d · outbound

This paper cites Friedman, J., Hastie, T., Tibshirani, R.,.

Non-smooth optimization meets automated material model discovery Friedman, J., Hastie, T., Tibshirani, R.,

Reference 1

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Observation b3c240a7-2d70-4f9d-8d0f-cc35d08dcbd4 · outbound

This paper cites Linka, K., Hillg ¨artner, M., Abdolazizi, K.P., Aydin, R.C., Itskov, M., Cyron, C.J.,.

Non-smooth optimization meets automated material model discovery Linka, K., Hillg ¨artner, M., Abdolazizi, K.P., Aydin, R.C., Itskov, M., Cyron, C.J.,

Reference 4

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Observation dbeb3ae2-c42f-4c52-9270-2524ea1f2ebe · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 425, 116967.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 425, 116967

Reference 5

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Observation 881b97a0-da4d-4fe3-b5d6-f56951f25dbc · outbound

This paper cites Bomarito, G., Townsend, T., Stewart, K., Esham, K., Emery, J., Hochhalter, J.,.

Non-smooth optimization meets automated material model discovery Bomarito, G., Townsend, T., Stewart, K., Esham, K., Emery, J., Hochhalter, J.,

Reference 6

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Observation ca911a27-86ef-47a1-bfab-80acc6504f58 · outbound

This paper cites Science Advances 7, eabf3658.

Non-smooth optimization meets automated material model discovery Science Advances 7, eabf3658

Reference 8

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Observation 3cab19ce-e21c-4c8e-aabb-507ba1431f17 · outbound

This paper cites an unresolved cited work.

Non-smooth optimization meets automated material model discovery Unresolved cited work

Reference 12

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Observation b77390d0-e62e-4866-a7f4-fb769a2e8122 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 381, 113852.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 381, 113852

Reference 13

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Observation 5e16115f-ad46-416f-9109-14248ce166c8 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 388, 114217.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 388, 114217

Reference 19

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Observation 5105c470-dad5-42a7-8f24-1168dca83f3c · outbound

This paper cites Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.

Non-smooth optimization meets automated material model discovery Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling

Reference 23

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Observation be202aa8-d37b-4e95-ae7a-fa417f76d130 · outbound

This paper cites Applications in Engineering Science 6, 100052.

Non-smooth optimization meets automated material model discovery Applications in Engineering Science 6, 100052

Reference 25

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Observation 2ff41c61-f645-4863-86d3-078a80615e56 · outbound

This paper cites Computational Mechanics 69, 213–232.

Non-smooth optimization meets automated material model discovery Computational Mechanics 69, 213–232

Reference 26

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Observation e87791ee-761f-493d-8854-f4ded2875ad5 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 304, 81–101.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 304, 81–101

Reference 28

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Observation 49c2a762-25f8-42c7-b1ce-bab8c6b9a8c3 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 428, 117053.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 428, 117053

Reference 29

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Observation 66914e98-e2ba-4724-98f6-77b8b3470a37 · outbound

This paper cites Journal of the Mechanics and Physics of Solids 159, 104703.

Non-smooth optimization meets automated material model discovery Journal of the Mechanics and Physics of Solids 159, 104703

Reference 30

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Observation 74181bfb-bef6-41c4-a914-5f1feb1b0be4 · outbound

This paper cites Journal of Computational Physics 429, 110010.

Non-smooth optimization meets automated material model discovery Journal of Computational Physics 429, 110010

Reference 32

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Observation 64152817-1c4c-440b-b0b4-5bbeb1fb911d · outbound

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Non-smooth optimization meets automated material model discovery Unresolved cited work

Reference 33

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Observation 1219433a-0771-4be4-92aa-525201e8c555 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 403, 115731.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 403, 115731

Reference 34

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Observation 3f4e5028-d5fe-452b-bbe7-03cdb1a42650 · outbound

This paper cites Extreme Mechan- ics Letters 70, 102181.

Non-smooth optimization meets automated material model discovery Extreme Mechan- ics Letters 70, 102181

Reference 35

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Observation 8e783f8d-1b97-4990-a211-45e038370862 · outbound

This paper cites Acta Bio- materialia 160, 134–151.

Non-smooth optimization meets automated material model discovery Acta Bio- materialia 160, 134–151

Reference 36

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Observation 6d51bf5d-ceb8-4321-aa7b-561801aed07a · outbound

This paper cites 32 Mechanics of Materials 181, 104643.

Non-smooth optimization meets automated material model discovery 32 Mechanics of Materials 181, 104643

Reference 37

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Observation 16938e30-6251-4a50-a7bd-11b9a38270c2 · outbound

This paper cites Journal of the Mechanics and Physics of Solids 147, 104277.

Non-smooth optimization meets automated material model discovery Journal of the Mechanics and Physics of Solids 147, 104277

Reference 38

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Observation 65b57f82-c98b-481e-973f-2978d0642ace · outbound

This paper cites International Journal for Numerical Methods in Engineering 125, e7481.

Non-smooth optimization meets automated material model discovery International Journal for Numerical Methods in Engineering 125, e7481

Reference 39

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This paper cites URL: https: //engrxiv.org/preprint/view/2961, doi:https://doi.org/10.31224/2961.

Non-smooth optimization meets automated material model discovery URL: https: //engrxiv.org/preprint/view/2961, doi:https://doi.org/10.31224/2961

Reference 40

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Observation 43010b4f-4ef0-43f0-a6dc-52949211e174 · outbound

This paper cites arXiv:2504.19494 URL: https://arxiv.org/abs/2504.19494.

Non-smooth optimization meets automated material model discovery arXiv:2504.19494 URL: https://arxiv.org/abs/2504.19494

Reference 41

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Observation 50a9edc1-f942-4f78-bf44-bcc3c8ddb3e8 · outbound

This paper cites Composites Part B: Engineering 216, 108831.

Non-smooth optimization meets automated material model discovery Composites Part B: Engineering 216, 108831

Reference 43

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Observation 9f0b8648-638b-4f53-9a2e-868b75615185 · outbound

This paper cites Computational Mechanics URL: https://link.springer.com/10.1007/s00466-024-02515-y , doi:10.1007/s00466-024-02515-y.

Non-smooth optimization meets automated material model discovery Computational Mechanics URL: https://link.springer.com/10.1007/s00466-024-02515-y , doi:10.1007/s00466-024-02515-y

Reference 44

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Observation a425de61-362d-43f6-8666-120466cfc39d · outbound

This paper cites Applied Soft Computing 1, 105–118.

Non-smooth optimization meets automated material model discovery Applied Soft Computing 1, 105–118

Reference 45

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Observation 3382ce91-e94d-4edc-8cb1-20a45a700648 · outbound

This paper cites International Journal for Numerical Methods in Engineering 124, 4802–4840.

Non-smooth optimization meets automated material model discovery International Journal for Numerical Methods in Engineering 124, 4802–4840

Reference 47

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Non-smooth optimization meets automated material model discovery Science 324, 81–85

Reference 49

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Observation 8845685d-27ed-4ecd-a299-c1c95a61d0bb · outbound

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Non-smooth optimization meets automated material model discovery Pierre, S.R., Linka, K., Kuhl, E., 2023a

Reference 50

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Observation 018b3d10-a99d-4171-bae0-34ed8a9958a1 · outbound

This paper cites Communications in Numerical Methods in Engineering 25, 53–63.

Non-smooth optimization meets automated material model discovery Communications in Numerical Methods in Engineering 25, 53–63

Reference 51

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Observation fc36c90c-dc87-4988-882f-03e3fcfeb6aa · outbound

This paper cites Com- puter Methods in Applied Mechanics and Engineering 398, 115248.

Non-smooth optimization meets automated material model discovery Com- puter Methods in Applied Mechanics and Engineering 398, 115248

Reference 52

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Observation 05322092-00ab-4170-a609-ec0d512617ae · outbound

This paper cites Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs).

Non-smooth optimization meets automated material model discovery Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)

Reference 53

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Observation 79ab0a31-954b-488f-b274-7ec5a6696a76 · outbound

This paper cites Journal of the Mechanics and Physics of Solids 169, 105076.

Non-smooth optimization meets automated material model discovery Journal of the Mechanics and Physics of Solids 169, 105076

Reference 54

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Observation 87fc6c33-d50b-4ca7-98b6-d0ec928fe091 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering 318, 981–1004.

Non-smooth optimization meets automated material model discovery Computer Methods in Applied Mechanics and Engineering 318, 981–1004

Reference 57

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Observation 4b22ceab-ea8d-4d00-ab9d-701260bacdb8 · outbound

This paper cites Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity.

Non-smooth optimization meets automated material model discovery Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity

Reference 58

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Observation 33841263-e5ab-4ecb-8d5f-badb1e3e22ae · outbound

This paper cites Journal of the Mechanics and Physics of Solids 153, 104474.

Non-smooth optimization meets automated material model discovery Journal of the Mechanics and Physics of Solids 153, 104474

Reference 59

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Observation 3c6ed3e8-6d8c-45ad-9ba8-b7ca98fd3d17 · outbound

This paper cites A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent.

Non-smooth optimization meets automated material model discovery A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent

Reference 60

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This paper cites Flaschel, M., Kumar, S., De Lorenzis, L., 2023a.

Non-smooth optimization meets automated material model discovery Flaschel, M., Kumar, S., De Lorenzis, L., 2023a

Reference 91

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Observation c38a2c60-8b39-4e07-b89b-e0c4bc076b27 · outbound

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Non-smooth optimization meets automated material model discovery Experiments on the deformation of rubber

Reference 1951

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Observation 704a1f80-bf13-4aac-a9e5-01c5ab1ad74c · outbound

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Non-smooth optimization meets automated material model discovery Proceedings of the Royal Society of London

Reference 1972

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Observation 6fe53933-df5e-451d-8c29-0628590150d6 · outbound

This paper cites SIAM Journal on Scientific and Statistical Computing 7, 1307–1330.

Non-smooth optimization meets automated material model discovery SIAM Journal on Scientific and Statistical Computing 7, 1307–1330

Reference 1986

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Observation 1f537b48-383d-499b-a817-0c7babdbc0a1 · outbound

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Non-smooth optimization meets automated material model discovery Journal of Engineer- ing Mechanics 117, 132–153

Reference 1991

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Non-smooth optimization meets automated material model discovery Technometrics 35, 109–135

Reference 1993

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Non-smooth optimization meets automated material model discovery Journal of the Royal Statistical Society: Series B (Methodological) 58, 267–288

Reference 1996

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This paper cites Fuhg, J.N., Anantha Padmanabha, G., Bouklas, N., Bahmani, B., Sun, W., Vlassis, N.N., Flaschel, M., Carrara, P., De Lorenzis, L., 2024a.

Non-smooth optimization meets automated material model discovery Fuhg, J.N., Anantha Padmanabha, G., Bouklas, N., Bahmani, B., Sun, W., Vlassis, N.N., Flaschel, M., Carrara, P., De Lorenzis, L., 2024a

Reference 1998

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Source-reported events for the cited work

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Observation 3ea73a20-4f65-410f-8b82-89437acf7cce · outbound

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Non-smooth optimization meets automated material model discovery International Journal of Solids and Structures 38, 7999–8018

Reference 2001

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Non-smooth optimization meets automated material model discovery Com- munications on Pure and Applied Mathematics 57, 1413–1457

Reference 2004

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Non-smooth optimization meets automated material model discovery Journal of the Royal Statistical Society: Series B (Statisti- cal Methodology) 67, 301–320

Reference 2005

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Non-smooth optimization meets automated material model discovery Rubber Chem- istry and Technology 79, 835–858

Reference 2006

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Observation cebc5497-bd86-46fe-969d-598ccfeb3547 · outbound

This paper cites IEEE Journal of Selected Topics in Signal Processing 1, 606–617.

Non-smooth optimization meets automated material model discovery IEEE Journal of Selected Topics in Signal Processing 1, 606–617

Reference 2007

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Source-reported events for the cited work

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Observation aaf6a784-8d0f-4286-b54e-8953efcf1b64 · outbound

This paper cites Springer Series in Statistics, Springer New York, New York, NY.

Non-smooth optimization meets automated material model discovery Springer Series in Statistics, Springer New York, New York, NY

Reference 2009

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Observation 73d9902c-90d4-4da2-b5ad-9a1729b2dfc7 · outbound

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Non-smooth optimization meets automated material model discovery Journal of Elasticity 120, 129–160

Reference 2015

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Observation 6eb1feb8-49b4-4cad-b368-50b4589a583a · outbound

This paper cites Proceedings of the National Academy of Sciences 113, 3932–3937.

Non-smooth optimization meets automated material model discovery Proceedings of the National Academy of Sciences 113, 3932–3937

Reference 2016

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Observation 662b729e-c92b-4873-a4af-a045e0ab0f33 · outbound

This paper cites Computational Mechanics 60, 813–826.

Non-smooth optimization meets automated material model discovery Computational Mechanics 60, 813–826

Reference 2017

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Observation 4ce42210-a121-49eb-86a5-0d561dc7c318 · outbound

This paper cites Science Advances 6, eaay2631.

Non-smooth optimization meets automated material model discovery Science Advances 6, eaay2631

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:59.182696Z

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Observation 6a80637a-ef68-43ee-8808-4b549a0ab902 · outbound

This paper cites Computers & Structures 252, 106557.

Non-smooth optimization meets automated material model discovery Computers & Structures 252, 106557

Reference 2021

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Observation d9d13e04-9586-4754-8bd2-70cb630950f0 · outbound

This paper cites net/10.13140/RG.2.2.21694.36168, doi:10.13140/RG.2.2.21694.36168.

Non-smooth optimization meets automated material model discovery net/10.13140/RG.2.2.21694.36168, doi:10.13140/RG.2.2.21694.36168

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:58.880669Z

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Observation 52d2ddef-c1d9-4263-b7e8-250dca3c3fd4 · outbound

This paper cites International Journal for Numerical Methods in Engineering , nme.7203URL: https://onlinelibrary.wiley.com/doi/10.1002/nme.

Non-smooth optimization meets automated material model discovery International Journal for Numerical Methods in Engineering , nme.7203URL: https://onlinelibrary.wiley.com/doi/10.1002/nme

Reference 2023

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Observation 4fa8074a-9d24-4311-8484-94bc02182027 · outbound

This paper cites International Journal for Numerical Methods in Engineering 125, e7473.

Non-smooth optimization meets automated material model discovery International Journal for Numerical Methods in Engineering 125, e7473

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:58.907431Z

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Observation cb1de51b-0182-45f5-9b84-51f0d9e72e96 · outbound

This paper cites Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling.

Non-smooth optimization meets automated material model discovery Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:58.723667Z

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Pith citing papers

Observation c3c831e8-2693-4845-9665-3839729030c8 · inbound

Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity cites this paper.

Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity Non-smooth optimization meets automated material model discovery

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T05:38:21.760932Z

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Observation 0904435a-4ea7-4b5c-a88e-7bae78000e95 · inbound

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data cites this paper.

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data Non-smooth optimization meets automated material model discovery

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-08-04T02:08:08.792719Z

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