Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-15T01:51:47.509518Z
Paper Citation Record · LEDGER
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables
As of 11 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2605.14179.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
37 of 37 outbound references displayed
- verified exact21
- verified fuzzy11
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 141c83a5-4dab-4c9f-94ad-93fa9b16c131 · outbound
This paper cites Zico Kolter.
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:29:53.927583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4ae79d13-57bf-4ee2-8abc-d3f4df017524 · outbound
This paper cites A mechanics-informed artificial neural network approach in data-driven constitutive modeling.International Journal for Numerical Methods in Engineering, 123 20 (12):2738–2759.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables A mechanics-informed artificial neural network approach in data-driven constitutive modeling.International Journal for Numerical Methods in Engineering, 123 20 (12):2738–2759
Reference 2
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.128092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e591934d-ce87-4af7-83f8-11ad8b8f710c · outbound
This paper cites As’ad and C.
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T01:53:28.119810Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 35293da2-75ac-4ed2-8639-fe8128152460 · outbound
This paper cites an unresolved cited work.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Unresolved cited work
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:53:28.125365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4fce5182-f1cb-49c5-83c1-02ebacfa40f8 · outbound
This paper cites an unresolved cited work.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-05-15T08:29:53.932490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a0937e3d-2812-4ec9-81f5-4d03a23a855b · outbound
This paper cites Archive for Rational Mechanics and Analysis63(4), 337–403 (1976) https://doi.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Archive for Rational Mechanics and Analysis63(4), 337–403 (1976) https://doi
Reference 6
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.113535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation fb35c671-32fa-4653-b288-5be6b6fae2df · outbound
This paper cites American Mathematical Soc.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables American Mathematical Soc
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:29:53.920334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0b52df16-f45f-4331-9cec-2d43e47be1dd · outbound
This paper cites Learning markovian homogenized models in viscoelasticity.Multiscale Modeling & Simulation, 21(2):641–679.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Learning markovian homogenized models in viscoelasticity.Multiscale Modeling & Simulation, 21(2):641–679
Reference 8
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.130368Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8ce88431-8602-4867-88e8-dcba98c41600 · outbound
This paper cites Brenner and P.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Brenner and P
Reference 9
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.115938Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 85d43abc-e56f-46c0-bb59-fbb56b7e76ec · outbound
This paper cites Coleman and Morton E.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Coleman and Morton E
Reference 10
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.121944Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d8ba8f90-2b6e-419f-8c65-dd8378c2a18f · outbound
This paper cites Convex neural networks learn generalized standard material models.Journal of the Mechanics and Physics of Solids, 200:106103.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Convex neural networks learn generalized standard material models.Journal of the Mechanics and Physics of Solids, 200:106103
Reference 11
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T01:53:28.167754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 931f32a5-f094-419d-b3b4-bcc956d5aaca · outbound
This paper cites Existence results for a class of rate-independent material models with nonconvex elastic energies.Journal f¨ ur die reine und angewandte Mathematik, 2006(595):55–91.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Existence results for a class of rate-independent material models with nonconvex elastic energies.Journal f¨ ur die reine und angewandte Mathematik, 2006(595):55–91
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.667360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a8148d9a-19ac-4d33-bcc4-c82230776c8d · outbound
This paper cites URLhttps://doi.org/10.1515/CRELLE.2006.044.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables URLhttps://doi.org/10.1515/CRELLE.2006.044
Reference 13
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.146583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 73c51a12-33c5-4d89-9b41-f7706e0e2f37 · outbound
This paper cites Francfort and Pierre M.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Francfort and Pierre M
Reference 14
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.141894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3ea044a2-81bf-479f-b18f-9b70fbc51637 · outbound
This paper cites Horstemeyer and Douglas J.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Horstemeyer and Douglas J
Reference 15
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.134973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f98f5de6-5a71-41d3-8a77-fb2c8cb5f61a · outbound
This paper cites InterdisciplinaryAppliedMathematics.SpringerNewYork.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables InterdisciplinaryAppliedMathematics.SpringerNewYork
Reference 16
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.137567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5f3b48d6-3c51-47fc-9f77-50c4092bb02c · outbound
This paper cites A learning-based multiscale model for reactive flow in porous media.Water Resources Research, 60(9):e2023WR036303.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables A learning-based multiscale model for reactive flow in porous media.Water Resources Research, 60(9):e2023WR036303
Reference 17
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.161269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6bac83d3-aa3d-480d-955d-fab9e3f1bfc6 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Adam: A Method for Stochastic Optimization
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T01:53:29.022018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b0caa035-e8cc-4844-bd12-c1036519509b · outbound
This paper cites K., Fern´ andez, M., Martin, R.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables K., Fern´ andez, M., Martin, R
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:53:28.164652Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c4b4dc02-1588-49b5-9a9c-8c09e5e1bf70 · outbound
This paper cites Cambridge university press.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Cambridge university press
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.678031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6a9dd71e-7ad5-4804-b5e4-87addc7bdd81 · outbound
This paper cites Multiscale modeling of materials: Computing, data science, uncertainty and goal- oriented optimization.Mechanics of Materials, 165:104156.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Multiscale modeling of materials: Computing, data science, uncertainty and goal- oriented optimization.Mechanics of Materials, 165:104156
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:53:28.170959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ef171d41-3472-466c-9e20-2e841db43cf4 · outbound
This paper cites Learning chaotic dynamics in dissipative systems.Advances in Neural Information Processing Systems, 35:16768–16781.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Learning chaotic dynamics in dissipative systems.Advances in Neural Information Processing Systems, 35:16768–16781
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.675202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9e914d86-ac80-4160-99e4-9a41bd60ee67 · outbound
This paper cites Stuart, and Kaushik Bhattacharya.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Stuart, and Kaushik Bhattacharya
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:53:28.156144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e07945aa-e20f-4ac4-a641-584627d64470 · outbound
This paper cites EvolutionTANNandtheidentificationofinternalvariablesandevolutionequationsinsolid mechanics.Journal of the Mechanics and Physics of Solids, 174:105245.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables EvolutionTANNandtheidentificationofinternalvariablesandevolutionequationsinsolid mechanics.Journal of the Mechanics and Physics of Solids, 174:105245
Reference 24
- Resolution
- malformed identifier
- doi_truncated, observed 2026-05-15T01:53:28.144451Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c98cd1c3-fc39-4d46-bd56-0b0cf3d9110b · outbound
This paper cites Existence results for energetic models for rate-independent systems.Calculus of Variations and Partial Differential Equations, 22(1):73–99, Jan 2005.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Existence results for energetic models for rate-independent systems.Calculus of Variations and Partial Differential Equations, 22(1):73–99, Jan 2005
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:29:53.936773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 250536d1-a011-4103-8750-25b0ccb981e2 · outbound
This paper cites URLhttps://doi.org/10.1007/s00526-004-0267-8.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables URLhttps://doi.org/10.1007/s00526-004-0267-8
Reference 26
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.148974Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6b5b8cfb-b1bd-4e77-a4d5-d5221a8e9e8f · outbound
This paper cites Mielke, Energetic formulation of multiplicative elasto-plasticity using dissipation distances, Contin.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Mielke, Energetic formulation of multiplicative elasto-plasticity using dissipation distances, Contin
Reference 27
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.150936Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7192fe47-aaf9-4464-986f-8930b6980f93 · outbound
This paper cites Existence of minimizers in incremental elasto-plasticity with finite strains.SIAM Journal on Mathematical Analysis, 36(2):384–404.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Existence of minimizers in incremental elasto-plasticity with finite strains.SIAM Journal on Mathematical Analysis, 36(2):384–404
Reference 28
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.132497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation dbbc8661-5dc5-4ef1-bb36-b6296abd96f6 · outbound
This paper cites Global existence results for viscoplasticity at finite strain.Archive for Rational Mechanics and Analysis, 227(1):423–475, Jan 2018.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Global existence results for viscoplasticity at finite strain.Archive for Rational Mechanics and Analysis, 227(1):423–475, Jan 2018
Reference 29
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.158800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e4caaacb-00e5-499c-986d-6f2db6abe903 · outbound
This paper cites Computer Methods in Applied Mechanics and Engineering171(3–4), 419–444 (1999) https://doi.org/10.1016/S0045-7825(98)00219-9.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Computer Methods in Applied Mechanics and Engineering171(3–4), 419–444 (1999) https://doi.org/10.1016/S0045-7825(98)00219-9
Reference 30
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.139667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5295c6f7-fcd0-4b22-a1bf-a27ebba2fd12 · outbound
This paper cites Pytorch: An imperative style, high-performance deep learning library.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Pytorch: An imperative style, high-performance deep learning library
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.662053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f73f2967-41c2-4ea1-8cda-bfb605621afd · outbound
This paper cites Springer Science & Business Media.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Springer Science & Business Media
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.664529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 54694198-b2ec-4a10-b4c7-ed110c4777e0 · outbound
This paper cites Approximation theory of the mlp model in neural networks.Acta numerica, 8:143–195.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Approximation theory of the mlp model in neural networks.Acta numerica, 8:143–195
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.670157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation af22ea1b-a020-4882-acac-278d817731b6 · outbound
This paper cites an unresolved cited work.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Unresolved cited work
Reference 34
- Resolution
- unresolved
- raw_fallback, observed 2026-05-15T08:20:18.672545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation fd85ff45-9f47-42b8-88b3-5ad803fac176 · outbound
This paper cites URLhttps://www.sciencedirect.com/ science/article/pii/002250967190010X.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables URLhttps://www.sciencedirect.com/ science/article/pii/002250967190010X
Reference 35
- Resolution
- verified exact
- doi, observed 2026-05-15T01:53:28.153097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5f91b1cd-6718-4b74-81fc-ce11ccace838 · outbound
This paper cites Polyconvex energies for trigonal, tetragonal and cubic symmetry groups.
A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables Polyconvex energies for trigonal, tetragonal and cubic symmetry groups
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.659668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 2f53df6e-eca4-4413-8fbb-c4b961c7fec2 · outbound
This paper cites Springer.
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-15T08:20:18.657422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
No inbound Pith citation observations are available.