Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization | Paper Citation Record

Observation 9200d979-aede-4760-bb83-0398d93b5751 · outbound

This paper cites Lemaitre, J.-L.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Lemaitre, J.-L

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:55.613987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d49b2883-eef9-4f76-ac43-d78e0cd443b9 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:55.702319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc5b622f-3666-430c-bfd8-33cb7342f44c · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:55.812768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc7ed134-36f8-491e-90cc-6629cffc4707 · outbound

This paper cites Hill, The mathematical theory of plasticity, V ol.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Hill, The mathematical theory of plasticity, V ol

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:55.881846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a90b3b9-a339-43af-a453-b282da03c2bb · outbound

This paper cites Truesdell, W.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Truesdell, W

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:55.951434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3dd405f-2b97-4250-a535-e94093bd7443 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.036792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5722fc14-1a8d-4ead-8a6d-ebef27f31152 · outbound

This paper cites Ghaboussi, D.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Ghaboussi, D

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.110949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcc4f69e-bf60-4ef3-8708-db3f639c9018 · outbound

This paper cites Bessa, R.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Bessa, R

Reference 8

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.493085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70bc34b1-2d47-4d70-b320-6f0164c7f5e5 · outbound

This paper cites Mozaffar, R.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Mozaffar, R

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.294829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd2cea0b-7fe1-44f3-bba9-5e3cbd1cad0b · outbound

This paper cites Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.385959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6dab950-9fa4-4e51-b016-c32c5ec9c0bf · outbound

This paper cites Zhang, Q.-J.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Zhang, Q.-J

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.485371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d05a949f-9c74-485e-be8e-5092e02f97a4 · outbound

This paper cites Linden, D.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Linden, D

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.597076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 047f2d70-1d91-4908-b695-d2401d898de7 · outbound

This paper cites Wang, L.-y.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Wang, L.-y

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.710379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad85f723-8e74-4fb6-b1f6-5e3ced2e5cf3 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.770254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b594a453-a01f-41d7-87aa-aa3de053cdc1 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.864208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7e8045c-0f3c-43d6-b457-732ff7517c5a · outbound

This paper cites Borkowski, C.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Borkowski, C

Reference 16

Resolution: malformed identifier; no resolver link, observed 2026-08-03T15:58:56.913490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c58b6fe0-958d-45aa-bb90-9057ba23f3d4 · outbound

This paper cites Eghbalian, M.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Eghbalian, M

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:56.958798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27bb322-bff7-455d-8581-cb0a4fd3a66b · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 18

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.462565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60127995-6720-45c3-b3f7-52f82b8712f2 · outbound

This paper cites Bigger, B.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Bigger, B

Reference 19

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.448703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6d7a87c7-eec5-40b1-84d1-54744fbb69f3 · outbound

This paper cites Leclerc, J.-N.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Leclerc, J.-N

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:57.280483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a56a8e58-89a7-4e85-b0a0-2ab03c8325af · outbound

This paper cites Denys, S.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Denys, S

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:57.359053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db117d62-6a86-436a-81d2-f548d9e2eb82 · outbound

This paper cites Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-03T16:03:25.436098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f59114fa-cc3d-4701-973b-39d68b0243b7 · outbound

This paper cites Pierron, Material Testing 2.0: A brief review, Strain 59 (3) (2023) e12434, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/str.12434.doi:10.1111/str.12434.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Pierron, Material Testing 2.0: A brief review, Strain 59 (3) (2023) e12434, _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/str.12434.doi:10.1111/str.12434

Reference 23

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.419922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b5839b6b-dcd3-4b28-b4c1-538b16982c6f · outbound

This paper cites Guélon, E.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Guélon, E

Reference 24

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.407604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 127b82b9-e12d-485e-82ad-853143624fe2 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:57.750052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ff48140-b5b7-4971-a472-7dc60c08630f · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:57.903490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4858fe9c-5b66-4bd8-8ea9-c786b836d629 · outbound

This paper cites Grédiac, Principe des travaux virtuels et identification, Comptes rendus de l’Académie des sciences.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Grédiac, Principe des travaux virtuels et identification, Comptes rendus de l’Académie des sciences

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.091084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5819312-4a4f-4b35-9c4e-d6a3d8c1bb37 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 28

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.395579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 705bb66e-4766-4109-aabe-40745c746e76 · outbound

This paper cites Lefik, B.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Lefik, B

Reference 29

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.384760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e37514d8-f25f-4313-b705-3873442f8d9d · outbound

This paper cites Mathieu, H.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Mathieu, H

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.418463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb1284d9-9aef-4353-84fd-5b236d0653d4 · outbound

This paper cites Gerbig, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Gerbig, A

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.503279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf3a450-f7ca-4454-af18-c9fa7158ecc2 · outbound

This paper cites Thakolkaran, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Thakolkaran, A

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.626601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36de10b6-6a58-409d-90f7-783143aaf220 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.671204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d5d9509-c2ed-4f1c-a6a1-4830d029c50a · outbound

This paper cites Akerson, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Akerson, A

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.835907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7637073f-3b93-4dec-b46e-9e31379424d2 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:58.943908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3f5e3a-cbc8-4c39-87a4-95ab3c62182b · outbound

This paper cites Demmerle, J.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Demmerle, J

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.052874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c0c6521-ff4f-4fe3-8634-6b79a9a44e6c · outbound

This paper cites Creuziger, M.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Creuziger, M

Reference 37

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.358511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d99d0d63-be70-4f8b-95fb-6822eb76de6b · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 38

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.347743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 024eced5-6aa8-4c3b-9198-2143eb4e6092 · outbound

This paper cites Chapelier, R.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Chapelier, R

Reference 39

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.336761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a466273-9b00-41bb-aa4a-794613eab247 · outbound

This paper cites Souto, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Souto, A

Reference 40

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.325069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e125f829-0290-4e1e-b770-66c3cc7068a8 · outbound

This paper cites Andrade-Campos, J.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Andrade-Campos, J

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.424914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8109d571-ac85-4271-b4cf-3892a85c5023 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.490289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb0f58e-967a-4845-8901-362629c80007 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.550543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423630be-557b-41db-a7cd-205b60c07f89 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 44

Resolution: malformed identifier; no resolver link, observed 2026-08-03T15:58:59.624862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124061a0-f78f-4c86-a184-f217243e95c7 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.685656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f55892-9ee6-4c79-8900-fe400c10b780 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.751338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a797d14-938c-4d4a-9590-fedd04cc7e2c · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.797646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c773a839-de3d-4d83-bfe3-31cdc21874f6 · outbound

This paper cites Chamoin, C.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Chamoin, C

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.836281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f11c93e2-3272-4ceb-ab4a-7b7fb3f36845 · outbound

This paper cites Barroqueiro, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Barroqueiro, A

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T15:58:59.889992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d453c0-5018-4869-9433-d7afc7574978 · outbound

This paper cites Gonçalves, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Gonçalves, A

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.012062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c66425f-a297-4f8c-8788-6e39db82b3db · outbound

This paper cites Gonçalves, A.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Gonçalves, A

Reference 51

Resolution: malformed identifier; no resolver link, observed 2026-08-03T15:59:00.083403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c1b7cb7-a048-4816-afa4-2fcbf57fc902 · outbound

This paper cites Ghouli, M.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Ghouli, M

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.147107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c1549c4-ed87-43a5-ab7a-75186680bf97 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 53

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.292694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95a0ea70-8788-455d-b0c5-95bc6542996e · outbound

This paper cites Maute, S.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Maute, S

Reference 54

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.281637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e672e86e-098e-4807-b1d6-a0429b40b8ba · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 55

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.270137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c034997d-b41f-4ec8-92b4-d6a41d73ba92 · outbound

This paper cites Alberdi, G.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Alberdi, G

Reference 56

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.258777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6cd3f009-2d37-4a15-baa2-62a65ca62875 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 57

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.247875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d0dbb2fd-503e-488b-a6fb-923bed0a9c46 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.551181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79a6c652-712e-4888-9352-a3b3c218f010 · outbound

This paper cites Multimaterial topology optimization for finite strain elastoplasticity: theory, methods, and applications.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Multimaterial topology optimization for finite strain elastoplasticity: theory, methods, and applications

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-03T16:03:25.236431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 16bb72a7-e88a-42ee-8079-9c4acd72ccb9 · outbound

This paper cites Bradbury, R.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Bradbury, R

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.673480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 387bc029-e3ef-4b29-8552-18dbd4c9c995 · outbound

This paper cites Svanberg, The method of moving asymptotes—a new method for structural optimization, International journal for numerical methods in engineering 24 (2) (1987) 359–373.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Svanberg, The method of moving asymptotes—a new method for structural optimization, International journal for numerical methods in engineering 24 (2) (1987) 359–373

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.720954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a522eb38-10bb-41c9-974d-a2ecbc4bdf01 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 62

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.220272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b23edf32-2417-4235-862d-816b3514e837 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 63

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.209871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f292d764-33f2-4bde-9292-ae9c5807787d · outbound

This paper cites Sigmund, On benchmarking and good scientific practise in topology optimization, Structural and Multidisci- plinary Optimization 65 (11) (2022) 315.doi:10.1007/s00158-022-03427-2.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Sigmund, On benchmarking and good scientific practise in topology optimization, Structural and Multidisci- plinary Optimization 65 (11) (2022) 315.doi:10.1007/s00158-022-03427-2

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:00.941507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f32845ca-fcb2-482d-aef7-b4403c3df342 · outbound

This paper cites Chung, C.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Chung, C

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:01.050474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 958a5eb8-1264-49f7-9f07-771c1ed8cf6b · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:01.198418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8488dddf-3e98-4210-88bb-00cb4230d048 · outbound

This paper cites an unresolved cited work.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Unresolved cited work

Reference 67

Resolution: verified exact; doi, observed 2026-08-03T16:03:25.191393Z

Source-reported events for the cited work

correction dated 2024-01-04. Source: crossref record 10.1038/s41467-023-44462-x->10.1038/s41467-023-42992-y:correction, observed 2026-07-11T03:02:12.927492+00:00. This notice travels one citation hop only.

Observation 183a0777-843e-487b-92ef-01bff03f4bd6 · outbound

This paper cites Sigmund, Morphology-based black and white filters for topology optimization, Structural and Multidisciplinary Optimization 33 (4) (2007) 401–424.

Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization Sigmund, Morphology-based black and white filters for topology optimization, Structural and Multidisciplinary Optimization 33 (4) (2007) 401–424

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T15:59:01.423822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers