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Paper Citation Record · LEDGER

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids

As of 21 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 1 inbound Pith citation observation for arXiv:2508.05638.

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

pith.paper-citation-record.v1
2508.05638 v1

Coverage vector

measured 77 of 77 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:03:13.013996Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T18:09:17.507069Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

77 of 77 outbound references displayed

  • verified exact0
  • verified fuzzy73
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 075e813e-05c3-4f63-8112-237ee2d65024 · outbound

This paper cites Data-driven continuum damage mechanics with built-in physics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Data-driven continuum damage mechanics with built-in physics,

Reference 1

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raw_fallback, observed 2026-08-06T15:03:13.810568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.761015Z digest=sha256:bb4ea97090324fba168611d097312dd5816240dc3703bd170d0e8f31decadb66

Observation 8ba1b786-44ee-4b39-a590-bdda518779e3 · outbound

This paper cites Comparison of discontinuous damage models of mullins-type,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Comparison of discontinuous damage models of mullins-type,

Reference 2

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raw_fallback, observed 2026-08-06T15:03:13.800789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.765016Z digest=sha256:fd7e0c4a99615b00def176f3424848c954df7e6496fec4088bab0fa72aa44935

Observation 05a42a3c-b6da-4b77-8015-d4d63ba28c09 · outbound

This paper cites A review on data-driven constitutive laws for solids,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A review on data-driven constitutive laws for solids,

Reference 3

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raw_fallback, observed 2026-08-06T15:03:13.791134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.768373Z digest=sha256:56a709159c7f47e93124a3c0f32fb24a77cf94b47d3f8d34dc45fcc2509481eb

Observation 29dc43d3-874b-4a9f-90a4-aba5518ac54e · outbound

This paper cites Rupture time under creep conditions,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Rupture time under creep conditions,

Reference 4

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raw_fallback, observed 2026-08-06T15:03:13.781317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.771759Z digest=sha256:f8fcbaf5c29a0d9e6fb64eeee7ec16c40d4a3793ed7124eb3485964e3fdf1c98

Observation cabc15fc-908a-4b63-841a-c1e16c2c11dd · outbound

This paper cites an unresolved cited work.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Unresolved cited work

Reference 5

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raw_fallback, observed 2026-08-06T15:03:13.770550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.775439Z digest=sha256:732ac6b590f1ef8af1e71bbc1399a264c79235ce794aec6b04f0640fbba8aba8

Observation 768716bd-56ce-48b9-bbbf-48288215c064 · outbound

This paper cites The effect of creep constitutive and damage relationships upon the rupture time of a solid circular torsion bar,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids The effect of creep constitutive and damage relationships upon the rupture time of a solid circular torsion bar,

Reference 6

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raw_fallback, observed 2026-08-06T15:03:13.761053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.779094Z digest=sha256:c7f7876d15b673c7d5f25252550a1cfc2485cd4d6f2e0382630ff1ebb604c43d

Observation 7c2ec13e-46e2-4e9e-af0f-890126594c17 · outbound

This paper cites Behavior of rubber under repeated stresses,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Behavior of rubber under repeated stresses,

Reference 7

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.782649Z digest=sha256:63603eb4b834b1f7a2b0310e826f884965ceca113a05aed91d1308b3e50e02bc

Observation 7c61eb7d-e570-4925-a14e-a3e3b54fb9ef · outbound

This paper cites Effect of stretching on the properties of rubber,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Effect of stretching on the properties of rubber,

Reference 8

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raw_fallback, observed 2026-08-06T15:03:13.741222Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.785772Z digest=sha256:36b57fe11a8d3b58748d29dc2dec3d02642762a1ee99fafcc1939b07e4c2d332

Observation bf1322bf-5761-424a-8f89-93f067819792 · outbound

This paper cites Theoretical model for the elastic behavior of filler-reinforced vulcanized rubbers,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Theoretical model for the elastic behavior of filler-reinforced vulcanized rubbers,

Reference 9

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raw_fallback, observed 2026-08-06T15:03:13.730815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.788984Z digest=sha256:315880dd405db85a75b3dbdafebc7b696f352b8718bd46e9b011fa0cb6fdfb69

Observation a75ca535-591d-41e6-aa28-bb55ca60d55d · outbound

This paper cites Double-network hydrogels with extremely high mechanical strength,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Double-network hydrogels with extremely high mechanical strength,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.720789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.791915Z digest=sha256:57902010d075b9484aee166b727e01840d9344645777311318d343ac1e407b20

Observation be042ecb-3228-4183-9d05-aa3364f31478 · outbound

This paper cites Induced anisotropy by mullins effect in filled elastomers subjected to stretching with various geometries,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Induced anisotropy by mullins effect in filled elastomers subjected to stretching with various geometries,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.711024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.795280Z digest=sha256:229a0dd133eb3916f6f1e46cab4207a76020afe4bf2a5a08af02dc24a8b0077b

Observation f09678cd-af33-4012-9062-b1b94d2a3bed · outbound

This paper cites Distinctive characteristics of internal fracture in tough double network hydrogels revealed by various modes of stretching,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Distinctive characteristics of internal fracture in tough double network hydrogels revealed by various modes of stretching,

Reference 12

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.701206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.798291Z digest=sha256:69c004830cec37def8fa9b16666022e67191bf08eba527b099bca1b84790eb0b

Observation 19a90489-8a02-45a5-8a22-f30fa58948ca · outbound

This paper cites Toughening elastomers with sacrificial bonds and watching them break,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Toughening elastomers with sacrificial bonds and watching them break,

Reference 13

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raw_fallback, observed 2026-08-06T15:03:13.690374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.801383Z digest=sha256:232d9ae3bbfd43694562e7a27307886192013e815c0637b11cceb8c8daccd84a

Observation 4ac6ed91-7477-4955-a1cb-b190cf5e48b6 · outbound

This paper cites Mechanics of elastomeric molecular composites,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Mechanics of elastomeric molecular composites,

Reference 14

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raw_fallback, observed 2026-08-06T15:03:13.679356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.805062Z digest=sha256:d274121487223bfdaaae7721fecc7b2cf6e80b0ffe658d7590a374821b8456a2

Observation e4bd84ed-2aa6-4e60-9965-fb539ab17a85 · outbound

This paper cites A micro-mechanically based continuum damage model for carbon black-filled rubbers incorporating mullins’ effect,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A micro-mechanically based continuum damage model for carbon black-filled rubbers incorporating mullins’ effect,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.669328Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.808058Z digest=sha256:83b86934e447821aa6281091aff75f28eff5e13d335e191164dc3fb264ff8e53

Observation eceb7a0b-7d66-4ce8-a8a0-2fff34d2cde7 · outbound

This paper cites The constitutive equations of continuum creep damage mechanics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids The constitutive equations of continuum creep damage mechanics,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.659512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.811307Z digest=sha256:aafb2d2c1db49704933bf23e9c4cdf2aaf027b7f01b65aa6974bc6bd0360699d

Observation 06256f5b-c4c1-4b3a-93e6-0abf54347dad · outbound

This paper cites Anisotropic damage in elasticity and plasticity,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Anisotropic damage in elasticity and plasticity,

Reference 17

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raw_fallback, observed 2026-08-06T15:03:13.648629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.814515Z digest=sha256:78572dd09de50b31ca4d4c0d9fcfc4df036652916296c39a32fc61f127d12ea2

Observation 08e02b22-04f4-488b-b1c4-791417613d61 · outbound

This paper cites Damage induced elastic anisotropy,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Damage induced elastic anisotropy,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.639449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.817793Z digest=sha256:eae0f114ef6eaf177090a2bc6da5cdb020b2e68e67727ebe4e7d4e58cf456590

Observation 35f2fab2-ea2d-466f-8e16-ac40443724b4 · outbound

This paper cites How to use damage mechanics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids How to use damage mechanics,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.629836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.820901Z digest=sha256:de1cfb05563ab7fdf4c86e7c26a78b24ba8992fef93a4cba6b250aba475a5633

Observation 704ea3ab-5d2c-4032-9736-dfada8a6dfaf · outbound

This paper cites A continuous damage mechanics model for ductile fracture,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A continuous damage mechanics model for ductile fracture,

Reference 20

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.620300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.824276Z digest=sha256:32250415ae82ba4b213d1c2a59259c4e3a40b951be37bed09c418969dc37b339

Observation d3c906aa-b0cf-4f4a-b77d-b2cedcfca311 · outbound

This paper cites Application of continuous damage mechanics to strain and fracture behavior of concrete,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Application of continuous damage mechanics to strain and fracture behavior of concrete,

Reference 21

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.610428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.827630Z digest=sha256:92467c33446f9ff2bdca1fed3707d0d607dddf039f37f5eb815b3f45c711ad6a

Observation d6fffc15-217f-4de1-9e5d-5dd0816302fe · outbound

This paper cites Strain-and stress-based continuum damage models—i. formulation,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Strain-and stress-based continuum damage models—i. formulation,

Reference 22

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.600542Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.830808Z digest=sha256:6b10152245a2fd1c8fb07c641d9a7086eaebe32ae8c3539820a3e54c66d35c17

Observation ae4e592a-37bf-4b1b-9d17-6a7a9dbf3b6b · outbound

This paper cites On energy-based coupled elastoplastic damage theories: constitutive modeling and computational aspects,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On energy-based coupled elastoplastic damage theories: constitutive modeling and computational aspects,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.589884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.834155Z digest=sha256:be69635e191e79bd9a43ef291dc8f6bec222bdfe3d718bad406e7491b39b260c

Observation c82b2b09-6f27-4def-880b-4fb947342d08 · outbound

This paper cites A unified theory of elastic degradation and damage based on a loading surface,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A unified theory of elastic degradation and damage based on a loading surface,

Reference 24

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.580560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.837653Z digest=sha256:86b679fd3142324ffffaa50b235ffdbb59921285869225d4a672eae629094407

Observation c2484777-fc5f-485d-babe-485d25d07a6f · outbound

This paper cites Tensorial nature of damage measuring internal variables,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Tensorial nature of damage measuring internal variables,

Reference 25

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raw_fallback, observed 2026-08-06T15:03:13.570364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.840611Z digest=sha256:bbf061bdd4ab0b20c3020b593a9f9b6fe9763c0a6b8544e3f6bead74a106c09b

Observation cca4a9e9-c86f-4060-99ab-f5cff14d4472 · outbound

This paper cites Representation of mechanical behavior in the presence of changing internal structure,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Representation of mechanical behavior in the presence of changing internal structure,

Reference 26

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raw_fallback, observed 2026-08-06T15:03:13.560033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.844088Z digest=sha256:920a9c6eb81a10463e08071bb119d75ee0991fbb7ac745670c33b43948d44691

Observation f9ce6b61-684e-4d42-b8f4-b535ec673474 · outbound

This paper cites Description of anisotropic damage application to elasticity,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Description of anisotropic damage application to elasticity,

Reference 27

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.549322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.847346Z digest=sha256:9ed3073f95ed0da4816a0f52c10a66a4f71b21ef711a14a92adea95fdd416ced

Observation 381e1d44-60af-40a7-a7e5-143684019200 · outbound

This paper cites On an anisotropic damage theory,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On an anisotropic damage theory,

Reference 28

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.539819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.850659Z digest=sha256:06eb5da94312d84724fb2ff6c9a7805020bd2680e80ac6902e56c1ffdda6be4f

Observation d74769a6-45cb-4860-8218-f5b3d5def016 · outbound

This paper cites An anisotropic theory of elasticity for continuum damage mechanics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids An anisotropic theory of elasticity for continuum damage mechanics,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.530851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.853927Z digest=sha256:4f16087ef98435628cde1c4bd6c917069973698e0f5e35f546b3e96954db19a5

Observation f6244ca8-0557-4e10-8fa6-9a0d908a8730 · outbound

This paper cites Isotropic and anisotropic damage variables in continuum damage mechanics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Isotropic and anisotropic damage variables in continuum damage mechanics,

Reference 30

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.521710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.857427Z digest=sha256:fe39544fdd769139810659be381cc3a8d4f3d34e1e4613c37625764837204d29

Observation e75bd5d1-cdd2-49da-a042-77726f8ae651 · outbound

This paper cites A new formulation of continuum damage mechanics (cdm) for composite materials,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A new formulation of continuum damage mechanics (cdm) for composite materials,

Reference 31

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raw_fallback, observed 2026-08-06T15:03:13.512134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.860570Z digest=sha256:ff24ff415757168b496e74f23788dc68c71bb46aa6e9a6b99fd1e178fe568999

Observation 584696b1-99f8-46e0-95f6-5d892b1eada6 · outbound

This paper cites Continuum damage modelling: approximation of crack induced anisotropy,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Continuum damage modelling: approximation of crack induced anisotropy,

Reference 32

Resolution
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raw_fallback, observed 2026-08-06T15:03:13.502417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.863686Z digest=sha256:fddeb28e434526ffefb732944d4d3e9fb66f92a08cfb47356d4fc65a06ebf172

Observation 388abf2d-8ead-4772-a370-43c5d357e5c0 · outbound

This paper cites On the formulation of anisotropic elastic degradation. i. theory based on a pseudo-logarithmic damage tensor rate,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On the formulation of anisotropic elastic degradation. i. theory based on a pseudo-logarithmic damage tensor rate,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.492737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.866661Z digest=sha256:cdca5dff74d120c9af08be4a777440b42a144490f3dcbc7ff6ea7a9672e11f88

Observation 746d8757-b793-4386-8a50-9b7a6df344ec · outbound

This paper cites Murakami, Continuum damage mechanics: a continuum mechanics approach to the analysis of damage and fracture, vol.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Murakami, Continuum damage mechanics: a continuum mechanics approach to the analysis of damage and fracture, vol

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.483309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.869991Z digest=sha256:4684277371da226cd0f65b45056d5fb67ee471c72d962a066368b0e941586cc9

Observation c48edbe7-63cb-4ba1-ac83-d8c11458d43a · outbound

This paper cites A continuum theory of creep and creep damage,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A continuum theory of creep and creep damage,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.473858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.873500Z digest=sha256:5d528363e0502ed0bd8bb3d56b8a23a111661d2d3f59bf8096692a403a30f4ba

Observation 6b4cb1c6-1621-4124-833b-b9db0eb4cdf6 · outbound

This paper cites Mechanical modeling of material damage,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Mechanical modeling of material damage,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.464420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.876678Z digest=sha256:e68f3d3737f24d3bd3362136b9873d9f3ae5d763e8ad4ecf657ba1fdabe2d99e

Observation c695a5ad-cc0b-450e-b374-70f51b16b467 · outbound

This paper cites A simple derivation of representations for non-polynomial constitutive equations in some cases of anisotropy,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A simple derivation of representations for non-polynomial constitutive equations in some cases of anisotropy,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.454167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.879822Z digest=sha256:a85ce06d6b2ffc0502b582576fa41f448101989eff1c5bd630d1e011bef3d9e5

Observation d4d41161-82cc-45e1-bb07-647222ae3ce0 · outbound

This paper cites On representations of anisotropic invariants,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On representations of anisotropic invariants,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.444819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.883206Z digest=sha256:d2fc925f2280f80fa6721698e74b4ee1308c16c82894eb699e4e82e111899abb

Observation 5a0e1444-0663-42dc-aa57-6d18afe037aa · outbound

This paper cites Theory of representations for tensor functions—a unified invariant approach to constitutive equations,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Theory of representations for tensor functions—a unified invariant approach to constitutive equations,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.435544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.886388Z digest=sha256:dc6c489e8c76f324c91febb7c9593120e66e3f92b9eaf90e01f5e78494689e24

Observation c35a594b-7df5-4297-b3cf-13b1b81796aa · outbound

This paper cites Structural tensors for anisotropic solids,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Structural tensors for anisotropic solids,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.425960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.889731Z digest=sha256:b916523196e8d84c36d11466b4b94b0cbba4b0201dd60591ddf02b5dd43b3d75

Observation 86ffb6a2-e743-4e81-aea0-7d145cec50b1 · outbound

This paper cites Itskov, Tensor algebra and tensor analysis for engineers, vol.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Itskov, Tensor algebra and tensor analysis for engineers, vol

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.416143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.893154Z digest=sha256:7a41bcc60dc0b9c90e6ca80e905a5a5186bfa8ab318f275e0b24078fd3f61413

Observation b82a3505-5748-4803-bcf2-9a34a1f79307 · outbound

This paper cites Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Invariant formulation of hyperelastic transverse isotropy based on polyconvex free energy functions,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.406629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.896281Z digest=sha256:28fc4363c8966ccc4ac1db016bf8428b795a44b0fc692bde8208b175423153a1

Observation 86bdc2e1-b4d4-4753-9a5a-15eb5e755d7c · outbound

This paper cites A class of orthotropic and transversely isotropic hyperelastic constitutive models based on a polyconvex strain energy function,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A class of orthotropic and transversely isotropic hyperelastic constitutive models based on a polyconvex strain energy function,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.396999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.899504Z digest=sha256:275ce9ff01de2bb0385d470e484e6bb601c04c54bfa21dd5f3c7c238e5c45bf8

Observation d5e8c513-9f68-42b0-981e-813659742e56 · outbound

This paper cites Anisotropic polyconvex energies on the basis of crystallographic motivated structural tensors,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Anisotropic polyconvex energies on the basis of crystallographic motivated structural tensors,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.386147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.902888Z digest=sha256:89479e3d205feb24fcbd7f084c83dd3b805cbb736e0d82555c5e4009286c3956

Observation 431811af-5ceb-4373-802b-54e0bf481732 · outbound

This paper cites On damage induced anisotropy for fiber composites,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On damage induced anisotropy for fiber composites,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.375056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.906357Z digest=sha256:007ffa35dc75c83d71f4a696572a2298fb6f401c49f48ed6ebc6bfafaedcc092

Observation 56339382-9587-466e-a7b2-9b77efefa9ae · outbound

This paper cites A framework for geometrically nonlinear continuum damage mechanics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A framework for geometrically nonlinear continuum damage mechanics,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.364413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.909884Z digest=sha256:7050feef445f6baf19b51047fcf3ba7c0d7dee3c983a929bb648bd2a94238d5a

Observation 25246cc4-f1a5-4792-b607-9efb96f249f9 · outbound

This paper cites A theoretical and computational framework for anisotropic continuum damage mechanics at large strains,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A theoretical and computational framework for anisotropic continuum damage mechanics at large strains,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.353753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.913143Z digest=sha256:a80c298ee8a8edd809aa10294738bd48064e297b44ae96d7f399b976d1ae4868

Observation d8256e26-23f2-4bee-ad8f-08f41de8b7a5 · outbound

This paper cites On the modelling of anisotropic elastic and inelastic material behaviour at large deformation,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On the modelling of anisotropic elastic and inelastic material behaviour at large deformation,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.343272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.916331Z digest=sha256:64f831091893191b33b39077df54b6439673636df75e7e11c2aff603a4898adf

Observation 9449609f-10b3-4f95-b998-c8c326c4b7c9 · outbound

This paper cites Using structural tensors for inelastic material modeling in the finite strain regime–a novel approach to anisotropic damage,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Using structural tensors for inelastic material modeling in the finite strain regime–a novel approach to anisotropic damage,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.332437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.919555Z digest=sha256:0175fb56c0a317561518482741632f40950e401dc5c62221a9953655ee9f7bca

Observation 4f3d663e-6fa2-43ae-b7ef-dbdcc60ab28e · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Multilayer feedforward networks are universal approximators,

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T15:03:12.922691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:03:12.922691Z digest=sha256:0c3edd2541292348bb88d6f42ec75565f9b0d980da003474e48085e6ec2bf0b3

Observation 2e6a4a45-d6c6-4782-9ca4-eb70a50fa437 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids TensorFlow: Large-scale machine learning on heterogeneous systems,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.315652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.925829Z digest=sha256:315a04ef9b06b6ce3120bdc5895ed73764a0db0678bd9d701adb6d321412947a

Observation d410a171-aa26-4e14-928c-61725e35423f · outbound

This paper cites JAX: composable transformations of Python+NumPy programs,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids JAX: composable transformations of Python+NumPy programs,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.305847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.929076Z digest=sha256:47884d05ac9ffbc029d4aedfeea194b46014eb0e3054896ec54d446cb09d7dad

Observation 6f48d3a1-cc7b-4b5e-811e-1c159706c322 · outbound

This paper cites Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.295274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.933174Z digest=sha256:d6c9ad89852474a87373980c52d47a4b0ef130c4e3990e1c11712fff91b107ac

Observation b3961559-c597-4748-9de3-d42fe9a49bd3 · outbound

This paper cites Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.283441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.936894Z digest=sha256:a3aa11f85137b6f0d428bfd66b255328fbdd88e9164825fe3f75fcfcfae64a8e

Observation 19420c48-ab8a-4c22-9c3f-6a815327b477 · outbound

This paper cites Polyconvex neural networks for hyperelastic constitutive models: A rectification approach,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Polyconvex neural networks for hyperelastic constitutive models: A rectification approach,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.273096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.940440Z digest=sha256:96aa24aa5a53d47982eda44b13daf7a6cc394a092e4e83e752b9286ac25b0cb0

Observation 1dd165da-1e4c-470e-b8da-f92ee7d886ac · outbound

This paper cites A mechanics-informed artificial neural network approach in data-driven constitutive modeling,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A mechanics-informed artificial neural network approach in data-driven constitutive modeling,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.263153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.943878Z digest=sha256:5ebea7dc4c75c9e3632de14254b67abf8f97764dc216356e3203f1465c5544ca

Observation 541bef9e-56dc-4ca9-a22c-80fe52d71ccc · outbound

This paper cites Learning hyperelastic anisotropy from data via a tensor basis neural network,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Learning hyperelastic anisotropy from data via a tensor basis neural network,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.252664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.947416Z digest=sha256:b7563b460daafae51d4621d909c24c5c8ea4c7f6b188c29de57b7c0b8ac936c3

Observation 0c19f99a-4374-45de-b76b-e27ef3ff116b · outbound

This paper cites Fe ann: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Fe ann: an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.242543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.950658Z digest=sha256:dc5282677b7594fce25c76b9c10f06eeb71b598ac22f63ee01aeb284b425b9cd

Observation 3fda42f2-1e15-4e18-bfb7-c85c6b51f3f7 · outbound

This paper cites Data-driven tissue mechanics with polyconvex neural ordinary differential equations,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Data-driven tissue mechanics with polyconvex neural ordinary differential equations,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.232363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.953980Z digest=sha256:3d5df1b2408b5c6dae53da35b79d88252d1bfaa14047684e4250151983bdf185

Observation 860b9b83-d517-4698-9ef2-e396fc564718 · outbound

This paper cites Neural networks meet hyperelasticity: A guide to enforcing physics,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Neural networks meet hyperelasticity: A guide to enforcing physics,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.221371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.957279Z digest=sha256:3d81575adee2f0f6e096d21b04b59bb670e6f45206f4eddb8fd52f9ae936e6ee

Observation 9988e1b1-835f-4644-94bd-758fbb328029 · outbound

This paper cites A new family of constitutive artificial neural networks towards automated model discovery,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids A new family of constitutive artificial neural networks towards automated model discovery,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.211313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.960319Z digest=sha256:997b10c36f9c568b2f9caf7552c56b9e70f865cf4ae2e659ad73611b163534bd

Observation 0a83da28-2f8d-49b8-8886-11e3abead7aa · outbound

This paper cites Benchmarking physics-informed frameworks for data-driven hyperelasticity,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Benchmarking physics-informed frameworks for data-driven hyperelasticity,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.201255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.963995Z digest=sha256:c4e7856b4f2187552a76a4552942bfeed40fcaaea1f481f802efc4f063b11218

Observation ac6a2b4e-d89e-48d7-abf7-af3b7bc23d1e · outbound

This paper cites Input convex neural networks,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Input convex neural networks,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.191272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.967320Z digest=sha256:e586dbb0a90c87e2afc20b1b27ad256e3478c1ae9c680a5ee1c34e835b5cd2ed

Observation 545f6800-e467-42ea-9f87-8f1dc0b1b878 · outbound

This paper cites Neural ordinary differential equations,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Neural ordinary differential equations,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.181497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.970704Z digest=sha256:63831019d56c9d17bd3d7625d335dff9c646b1de34f6d6ce10cf8ea8e01a585b

Observation 1098c3d3-fb70-434b-a454-7120780cebb1 · outbound

This paper cites Convexity conditions and existence theorems in nonlinear elasticity,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Convexity conditions and existence theorems in nonlinear elasticity,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.171343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.974029Z digest=sha256:b208304c09e8c1061e829de665152a7df3fc8ca9b554f0bc22848d7d7e7ce3f0

Observation 819ff003-f0e6-4893-a95e-7ecd61c62ed7 · outbound

This paper cites Recovering mullins damage hyperelastic behaviour with physics augmented neural networks,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Recovering mullins damage hyperelastic behaviour with physics augmented neural networks,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.161587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.977361Z digest=sha256:1d960c811192bd14cb14f2c5ee88125da508bbb503c679a8e4e5392330aaee49

Observation e754294c-3775-48e3-8158-0756270fdb60 · outbound

This paper cites On a fully three-dimensional finite-strain viscoelastic damage model: formulation and computational aspects,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On a fully three-dimensional finite-strain viscoelastic damage model: formulation and computational aspects,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.151796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.981288Z digest=sha256:de0b55e3ad1be93b8aa68d48deaa0e73c653ff20bfc28f9c912679242c3c0264

Observation 6cbbc185-35b4-4deb-8857-71b19c672875 · outbound

This paper cites Thermodynamics with internal state variables,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Thermodynamics with internal state variables,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.140800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T15:03:12.984546Z digest=sha256:40287267b1a479badbd0dfa2751be7a6634b0509e2ca62bcf9686f2d7e9a16f4

Observation d9cb793e-ad52-4e58-907c-2a3e93fa1bce · outbound

This paper cites Quasi-convexity and the lower semicontinuity of multiple integrals,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Quasi-convexity and the lower semicontinuity of multiple integrals,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:03:13.130513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation fe3ddb96-a960-4849-958f-086f19e30ea1 · outbound

This paper cites On a new class of elastic deformations not allowing for cavitation,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On a new class of elastic deformations not allowing for cavitation,

Reference 70

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Observation 50c3bb1a-4e07-4991-a2ac-ad310f7755c6 · outbound

This paper cites an unresolved cited work.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Unresolved cited work

Reference 71

Resolution
unresolved
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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d54ac4c2-5f4b-4956-a8a2-0a6d47ab7c49 · outbound

This paper cites Polyconvex anisotropic hyperelasticity with neural networks,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Polyconvex anisotropic hyperelasticity with neural networks,

Reference 72

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Observation 31ad93f5-a0b9-4a05-9068-72d42667d98c · outbound

This paper cites Lemaitre and J.-L.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Lemaitre and J.-L

Reference 73

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e96a4246-ef6f-43f5-a196-7e201ed64d26 · outbound

This paper cites an unresolved cited work.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Unresolved cited work

Reference 74

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 79233305-d6a0-4a62-bef7-c987ada4932f · outbound

This paper cites Modeling neurodegeneration in chronic traumatic encephalopathy using gradient damage models,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Modeling neurodegeneration in chronic traumatic encephalopathy using gradient damage models,

Reference 75

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1208f4ff-4bb6-448a-b67c-faabd21c8f88 · outbound

This paper cites On the implementation of finite deformation gradient-enhanced damage models,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids On the implementation of finite deformation gradient-enhanced damage models,

Reference 76

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7fc1b151-f99f-4997-ac48-17af8edfc1ba · outbound

This paper cites Damage models for soft tissues: a survey,.

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids Damage models for soft tissues: a survey,

Reference 77

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

Observation 6ac0a96e-dea2-4994-972d-a70a7d3d33ff · inbound

A Differentiable Framework for Gradient Enhanced Damage with Physics-Augmented Neural Networks in JAX-FEM cites this paper.

A Differentiable Framework for Gradient Enhanced Damage with Physics-Augmented Neural Networks in JAX-FEM A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids

Reference 1

Resolution
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arxiv_id, observed 2026-05-13T18:13:05.330026Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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