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Paper Citation Record · LEDGER

A physics-augmented neural network framework for finite strain incompressible viscoelasticity

As of 10 August 2026, this Paper Citation Record lists 100 of 110 outbound references and 4 inbound Pith citation observations for arXiv:2511.02959.

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

pith.paper-citation-record.v1
2511.02959 v1

Coverage vector

measured 100 of 110 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:11:44.636933Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T19:22:12.875861Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:58:43.070251Z

Reference resolution

100 of 110 outbound references displayed

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External citation measurements

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Outbound references

Observation 0e38f847-a743-489d-86e3-57314f127a1d · outbound

This paper cites Springer Berlin Heidelberg, Berlin, Heidelberg, 1997.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Springer Berlin Heidelberg, Berlin, Heidelberg, 1997

Reference 1

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Observation 9a7f1cec-5825-463a-8da4-e00dbf56bc6d · outbound

This paper cites Springer Berlin Heidelberg, Berlin, Heidelberg,.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Springer Berlin Heidelberg, Berlin, Heidelberg,

Reference 2

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Observation c7dc4318-7711-4f34-87b6-9c980ac46b3b · outbound

This paper cites Holzapfel.Nonlinear Solid Mechanics - A Continuum Approach for Engineering.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Holzapfel.Nonlinear Solid Mechanics - A Continuum Approach for Engineering

Reference 3

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Observation 2ed9484e-4cf7-4146-a859-0fb40389dffc · outbound

This paper cites Neural Networks for Con- stitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Neural Networks for Con- stitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics

Reference 4

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Observation df8259c0-c509-4008-b801-b88e319e38c4 · outbound

This paper cites Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N

Reference 5

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Observation 753fefac-35d5-4095-bb9a-8a8a0150975a · outbound

This paper cites Ghaboussi, J.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Ghaboussi, J

Reference 6

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Observation fab2ef26-e4f5-45c2-9b9f-92f81705a13c · outbound

This paper cites Frankel, Reese E.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Frankel, Reese E

Reference 7

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

Reference 8

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Observation aa7ee1fc-91f7-4211-bb4a-61e118cae034 · outbound

This paper cites Versatile data-adaptive hyperelastic energy functions for soft materials.Computer Methods in Applied Mechanics and Engineering, 430:117208, October 2024.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Versatile data-adaptive hyperelastic energy functions for soft materials.Computer Methods in Applied Mechanics and Engineering, 430:117208, October 2024

Reference 9

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Observation 1636a6d1-c359-41de-846e-00b5ae7bd5d1 · outbound

This paper cites Unsupervised discovery of interpretable hyperelastic constitutive laws.Computer Methods in Applied Mechanics and Engineering, 381:113852, August 2021.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unsupervised discovery of interpretable hyperelastic constitutive laws.Computer Methods in Applied Mechanics and Engineering, 381:113852, August 2021

Reference 10

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Observation f0aa628d-3dca-42d7-a6df-654ff188eede · outbound

This paper cites Automated discovery of generalized standard material models with EUCLID.Computer Methods in Applied Mechanics and Engineering, 405:115867, February 2023.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Automated discovery of generalized standard material models with EUCLID.Computer Methods in Applied Mechanics and Engineering, 405:115867, February 2023

Reference 11

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Observation 370b4975-cc0d-48c2-8155-d1c47485a75c · outbound

This paper cites Thermodynamically consistent neural network plasticity modeling and discovery of evolution laws.Journal of the Mechanics and Physics of Solids, 180:105416, November 2023.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Thermodynamically consistent neural network plasticity modeling and discovery of evolution laws.Journal of the Mechanics and Physics of Solids, 180:105416, November 2023

Reference 12

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Observation 7851eebe-ee28-47b0-9004-a0e936b4b3e7 · outbound

This paper cites Automatic generation of interpretable hyperelastic material models by symbolic regression.International Journal for Numerical Methods in Engineering, 124(9): 2093–2104, 2023.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Automatic generation of interpretable hyperelastic material models by symbolic regression.International Journal for Numerical Methods in Engineering, 124(9): 2093–2104, 2023

Reference 13

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Observation 61eadf18-0e9d-4baa-83fc-61b8f4ecd152 · outbound

This paper cites Bock, Roland C.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Bock, Roland C

Reference 14

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Observation 317c12f1-8a29-4407-8a44-3c1f058be13c · outbound

This paper cites A review of artificial neural networks in the constitutive modeling of composite materials.Composites Part B: Engineering, 224:109152, November 2021.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity A review of artificial neural networks in the constitutive modeling of composite materials.Composites Part B: Engineering, 224:109152, November 2021

Reference 15

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Observation 5dde22ed-f008-4f0a-b320-b434440d5577 · outbound

This paper cites Raissi, P.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Raissi, P

Reference 16

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Observation f3c94b46-3376-48a6-bdaa-8ad5e5d7627a · outbound

This paper cites Physics informed neural networks for continuum micromechanics.Computer Methods in Applied Mechanics and Engineering, 393:114790, 2022.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Physics informed neural networks for continuum micromechanics.Computer Methods in Applied Mechanics and Engineering, 393:114790, 2022

Reference 17

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Observation 5bada685-734a-46d8-82b1-7d3b22d3132e · outbound

This paper cites Kochmann.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kochmann

Reference 18

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Observation 18377ad9-dbd2-4dc2-8ade-303e70d55def · outbound

This paper cites A mechanics-informed artificial neural network approach in data-driven constitutive modeling.International Journal for Numerical Methods in Engineering, 123(12): 2738–2759, 2022.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity A mechanics-informed artificial neural network approach in data-driven constitutive modeling.International Journal for Numerical Methods in Engineering, 123(12): 2738–2759, 2022

Reference 19

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Observation 82f66d03-373f-4e8d-b25e-a3defb9e6973 · outbound

This paper cites Klein, Rogelio Ortigosa, Jesús Martínez-Frutos, and Oliver Weeger.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Klein, Rogelio Ortigosa, Jesús Martínez-Frutos, and Oliver Weeger

Reference 20

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Observation 6d75b41a-3b3e-423a-afa8-75b0635fc972 · outbound

This paper cites Klein, Karl A.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Klein, Karl A

Reference 21

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Observation 1fa88c98-4f5b-480c-87f5-16ab54de4c81 · outbound

This paper cites Elsayed, Yousef Heider, and Oliver Weeger.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Elsayed, Yousef Heider, and Oliver Weeger

Reference 22

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Observation 8cceece1-1fa1-4e32-bd58-8f8e6fe9848c · outbound

This paper cites Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios.Computer Methods in Applied Mechanics and Engineering, 444:118116, September 2025.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios.Computer Methods in Applied Mechanics and Engineering, 444:118116, September 2025

Reference 23

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This paper cites Kalina, Lennart Linden, Jörg Brummund, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Lennart Linden, Jörg Brummund, and Markus Kästner

Reference 24

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Observation 406a74c1-c2f3-49fc-b6b9-a9bfd21bd58b · outbound

This paper cites Thermodynamics-based Artificial Neural Networks for constitutive modeling.Journal of the Mechanics and Physics of Solids, 147:104277, 2021.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Thermodynamics-based Artificial Neural Networks for constitutive modeling.Journal of the Mechanics and Physics of Solids, 147:104277, 2021

Reference 25

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Observation 9c57a767-8623-43e0-a1a2-3fcf1d597010 · outbound

This paper cites Kalina, Lennart Linden, Jörg Brummund, Philipp Metsch, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Lennart Linden, Jörg Brummund, Philipp Metsch, and Markus Kästner

Reference 26

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Observation d73cdb22-1c22-4778-b7a6-8929e39b617e · outbound

This paper cites Abdolazizi, Roland C.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Abdolazizi, Roland C

Reference 27

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Observation 1ebe4396-d8c6-4aa1-951f-e5bdf2c9b4ff · outbound

This paper cites Kalina, Jörg Brummund, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Jörg Brummund, and Markus Kästner

Reference 28

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Observation fff12daf-af33-46cb-a815-7326305caa43 · outbound

This paper cites Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks.Computational Mechanics, April 2023.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks.Computational Mechanics, April 2023

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Observation ec98fee0-1cef-48ad-8a74-540b13848501 · outbound

This paper cites Multiscale modeling of viscoelastic shell structures with artificial neural networks.Computational Mechanics, March 2025.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Multiscale modeling of viscoelastic shell structures with artificial neural networks.Computational Mechanics, March 2025

Reference 30

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Observation 1f922ecf-b80d-4529-9691-0d0171f39ebb · outbound

This paper cites Hamel, Kyle Johnson, Reese Jones, and Nikolaos Bouklas.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Hamel, Kyle Johnson, Reese Jones, and Nikolaos Bouklas

Reference 31

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Observation a584f6e7-5364-4ca4-a72a-95d71622b130 · outbound

This paper cites Neural integration for constitutive equations using small data.Com- puter Methods in Applied Mechanics and Engineering, 420:116698, February 2024.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Neural integration for constitutive equations using small data.Com- puter Methods in Applied Mechanics and Engineering, 420:116698, February 2024

Reference 32

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Observation 68229205-ad78-4caf-a946-4b4b722d19f9 · outbound

This paper cites Klein, Mauricio Fernández, Robert J.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Klein, Mauricio Fernández, Robert J

Reference 33

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Observation 837f0947-d131-4d58-b5e8-53be9408b0c5 · outbound

This paper cites NN-EUCLID: Deep-learning hyperelasticity without stress data.Journal of the Mechanics and Physics of Solids, 169:105076, 2022.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity NN-EUCLID: Deep-learning hyperelasticity without stress data.Journal of the Mechanics and Physics of Solids, 169:105076, 2022

Reference 34

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Observation 9fb6501c-d625-4705-87a8-f243b80d7629 · outbound

This paper cites Fuhg, Nikolaos Bouklas, and Reese E.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Fuhg, Nikolaos Bouklas, and Reese E

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Benchmarking physics-informed frameworks for data-driven hyperelasticity.Computational Mechanics, 73(1):49–65, January

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Hurtado, and Ellen Kuhl

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Observation 4521a3de-f803-4e50-8587-abece499af76 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Sobolev Training for Neural Networks

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Observation dfc7e6d8-ef11-4285-9797-b4cabe2c05f2 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Vlassis, Ran Ma, and WaiChing Sun

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity doi:10.1002/nme.7473

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Polyconvex neural networks for hyperelastic constitutive models: A rectification approach.Mechanics Research Communications, 125:103993, 2022

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Observation f7872b9b-f659-4b31-9678-3e6bbfa01b48 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Jadoon, Karl A

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Observation e9f96514-8735-4b5a-8148-8494ab414d6b · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, and Markus Kästner

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This paper cites Kalina, Philipp Gebhart, Jörg Brummund, Lennart Linden, WaiChing Sun, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Philipp Gebhart, Jörg Brummund, Lennart Linden, WaiChing Sun, and Markus Kästner

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Number 516 in CISM Courses and Lectures

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Zico Kolter

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Observation c25ca965-aa1c-4ad4-bcec-b398618300ee · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Russ, Glaucio H

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Observation dc6622ab-974e-4468-bc62-685f7baab5ce · outbound

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Observation 093e465f-92ec-418b-bf43-b6fcd4472e83 · outbound

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Observation 30662fde-a146-492f-b4c9-565c305c1d9c · outbound

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Observation 3fbfba2e-8ce1-479b-b3d6-9abc2070c677 · outbound

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Observation dc607c77-21cc-44bc-ace5-96e57467889b · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Convex neural networks learn generalized standard material models.Journal of the Mechanics and Physics of Solids, 200:106103, July 2025

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Observation f6b86cef-2fa6-423b-9699-3cc77998d144 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Rausch, Francisco Sahli Costabal, and Adrian Buganza Tepole

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Observation ba338a48-f82e-4a06-8c5a-cfe058a18b3c · outbound

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Observation 0e1c4687-6353-47a3-b0cd-9185727f9882 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Jörg Brummund, WaiChing Sun, and Markus Kästner

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Observation 4be5fd63-bb07-47e5-9a01-620c7c256622 · outbound

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Observation 73ca7108-8763-4fd9-91d6-bf79750c8377 · outbound

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Observation 3b717fe8-9e5a-4247-ace3-a44198277f5b · outbound

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Observation 6fdbd7af-00fc-401b-8568-1be62c32ddb6 · outbound

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Observation 728a665c-6636-4452-8a99-51ad6eef308d · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity A theory of finite viscoelasticity and numerical aspects.International Journal of Solids and Structures, 35(26-27):3455–3482, September 1998

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Observation e6d38026-d88f-43f3-a9ef-f5ce9f491890 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 61812062-b58d-4c2e-a0f4-d5bea175ce03 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity On the two-potential constitutive modeling of rubber vis- coelastic materials.Comptes Rendus Mécanique, 344(2):102–112, February 2016

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Observation 3e0bb342-c9cb-450c-ba83-4b6b0b009417 · outbound

This paper cites Rambausek, D.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Rambausek, D

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Observation d3ca4b8d-6224-4db7-988d-ffe1d6a298aa · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Anisotropic evolution of viscous strain in soft biological materials.Mechanics of Materials, 192:104976, May 2024

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Observation e451625d-a53b-4a89-a0fa-23fca1c5e0c1 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 211973fe-e5e6-4a22-b7b0-9af34ba5dda4 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 49b67991-bb36-4a31-aabc-6fb2c6dcb735 · outbound

This paper cites Coleman and Walter Noll.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Coleman and Walter Noll

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Observation 00d339a3-5374-4081-958c-9e7f4e4f015a · outbound

This paper cites Coleman and Morton E.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Coleman and Morton E

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Observation 747373ac-3daf-44c0-965b-ea2f868d5906 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 27e1e663-d879-41e8-a21f-c18724eb9c27 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity A finite viscoelastic phase-field model for prediction of crack propagation speed in elastomers.European Journal of Mechanics - A/Solids, 113:105678, September 2025

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Observation 9676c6f7-428d-4b53-88ce-de19d6df3158 · outbound

This paper cites Kalina, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, and Markus Kästner

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Observation 7ca68abb-5b4e-4648-9d05-4f8dc0f28135 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation e43be29a-cc9e-486d-8ceb-5cbb941523b7 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 03a07363-9caf-4c68-b1a3-3201f2679e42 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 81499d5b-55bb-4dc7-bcea-e4f2476b4ccc · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 2b4d0d03-b234-4697-880f-a1c3ee5ee93a · outbound

This paper cites Klein, Fabian J.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Klein, Fabian J

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Observation 5651f6c5-0e4c-4b4f-848b-18ec8aa68230 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 8691a03c-4ddd-4299-9a21-3099b64ae5b6 · outbound

This paper cites Polyconvexity of generalized polynomial-type hyperelastic strain energy functions for near-incompressibility.International Journal of Solids and Structures, 40(11):2767–2791, 2003.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Polyconvexity of generalized polynomial-type hyperelastic strain energy functions for near-incompressibility.International Journal of Solids and Structures, 40(11):2767–2791, 2003

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Observation 9b037c81-0356-4997-a07d-c2fb214d6ed5 · outbound

This paper cites Kalina, Jörg Brummund, WaiChing Sun, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, Jörg Brummund, WaiChing Sun, and Markus Kästner

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Observation 1fbd9488-f78c-4949-84c6-8fde3af9b865 · outbound

This paper cites PhD thesis, University of Stuttgart, Stuttgart, 2007.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity PhD thesis, University of Stuttgart, Stuttgart, 2007

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Observation 00eb1930-d1a9-4fd8-b53a-50ebe9deb014 · outbound

This paper cites Springer International Publishing, Cham, 2021.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Springer International Publishing, Cham, 2021

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Observation 4eb0d191-2d2f-463c-b78d-8f4171f755c7 · outbound

This paper cites Thomas Seidl, Reese E.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Thomas Seidl, Reese E

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Observation 8facf514-4329-42d5-b3ca-0c61dd77a2e6 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 685850ab-64dd-4fd3-ab4a-b48653c1df76 · outbound

This paper cites Kalina, and Markus Kästner.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Kalina, and Markus Kästner

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Observation 3ee04fa5-f09a-4085-9b0d-ae8e1553f677 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Observation 1cd523fe-f56d-4618-a5d5-ceecfc49e681 · outbound

This paper cites On thermo-viscoelastic experimental characterization and numerical modelling of VHB polymer.International Journal of Non-Linear Mechanics, 118:103263, January 2020.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity On thermo-viscoelastic experimental characterization and numerical modelling of VHB polymer.International Journal of Non-Linear Mechanics, 118:103263, January 2020

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Observation 9f831820-124c-4e19-8f04-771f1eba8b33 · outbound

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A physics-augmented neural network framework for finite strain incompressible viscoelasticity Experimental study and numerical modelling of VHB 4910 polymer.Computational Materials Science, 59:65–74, June 2012

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Observation b1402a85-ad24-4204-806f-bc39395d96f8 · outbound

This paper cites an unresolved cited work.

A physics-augmented neural network framework for finite strain incompressible viscoelasticity Unresolved cited work

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Pith citing papers

Observation aed63338-f072-479d-8efa-293c8bfe0b86 · inbound

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling cites this paper.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling A physics-augmented neural network framework for finite strain incompressible viscoelasticity

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Observation bab8700b-6a00-40ed-b349-8a5ce5565727 · inbound

Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials cites this paper.

Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials A physics-augmented neural network framework for finite strain incompressible viscoelasticity

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Observation 5cf0e9a2-8839-4323-969c-961f32bbd32d · inbound

Sequential Subspace Mode Adaptation for the Reduced-Order Homogenization of Dissipative Microstructures using E3C Hyper-Reduction cites this paper.

Sequential Subspace Mode Adaptation for the Reduced-Order Homogenization of Dissipative Microstructures using E3C Hyper-Reduction A physics-augmented neural network framework for finite strain incompressible viscoelasticity

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arxiv_id, observed 2026-07-28T03:23:22.037721Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d0117577-7fc5-4997-90b2-d0c15886c9b5 · inbound

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling cites this paper.

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling A physics-augmented neural network framework for finite strain incompressible viscoelasticity

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arxiv_id, observed 2026-07-28T03:23:22.037721Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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