Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T17:44:43.544815Z
Paper Citation Record · LEDGER
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
As of 4 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 3 inbound Pith citation observations for arXiv:2604.07746.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T06:04:18.451578Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-30T15:04:46.460298Z
Reference resolution
60 of 60 outbound references displayed
- verified exact6
- verified fuzzy45
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dfc1a7ff-58ff-41da-a7cc-009b021bd500 · outbound
This paper cites MIT press Cambridge.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) MIT press Cambridge
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.159405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8b7aab0e-3629-4e75-a68f-26ab5e53209c · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.165775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation de7564b9-e2e1-4f34-a8a7-f58ec7b5ff47 · outbound
This paper cites Vlassis, Moritz Flaschel, Pietro Carrara, and Laura De Lorenzis.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Vlassis, Moritz Flaschel, Pietro Carrara, and Laura De Lorenzis
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.091112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d212fa85-2a53-4d19-b44b-98563e76cfb2 · outbound
This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Learning Sparse Neural Networks through $L_0$ Regularization
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.670367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 55d0f540-ed08-4436-9bdf-f8c06a18642c · outbound
This paper cites Pierre, Kevin Linka, and Ellen Kuhl.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Pierre, Kevin Linka, and Ellen Kuhl
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.156282Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b26090b1-8619-40ea-9657-9177bc17bb09 · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.096908Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b45860fa-3ed3-4401-a9c1-855d628241c6 · outbound
This paper cites A new family of constitutive artificial neural networks towards automated model discovery.Computer Methods in Applied Mechanics and Engineering, 403:115731.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) A new family of constitutive artificial neural networks towards automated model discovery.Computer Methods in Applied Mechanics and Engineering, 403:115731
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.100073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e0e4e1b9-1153-4270-94af-65543762405c · outbound
This paper cites Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.Computer Methods in Applied Mechanics and Engineering, 426:116973.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.Computer Methods in Applied Mechanics and Engineering, 426:116973
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.128853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4b52b5e1-6dab-4bcb-b048-b696e4251037 · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 9
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:14:21.683361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f2724a0c-e75f-412e-812f-ee606c3643d6 · outbound
This paper cites Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.697388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ff5f0ae0-9bb1-4579-9963-a1a94a92d968 · outbound
This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932– 3937.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the national academy of sciences, 113(15):3932– 3937
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.994639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 267ba500-47c0-4eca-bf47-b7b21e2e9324 · outbound
This paper cites Distilling free-form natural laws from experimental data.science, 324(5923):81– 85.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Distilling free-form natural laws from experimental data.science, 324(5923):81– 85
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.985642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 34727117-2f85-46a8-818c-1012429c0029 · outbound
This paper cites Ai feynman: A physics-inspired method for symbolic regression.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Ai feynman: A physics-inspired method for symbolic regression
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.998838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1edbccf1-9dc8-49ec-a75a-8e522198be0a · outbound
This paper cites Automated discovery of generalized standard material models with euclid.Computer Methods in Applied Mechanics and Engineering, 405:115867.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Automated discovery of generalized standard material models with euclid.Computer Methods in Applied Mechanics and Engineering, 405:115867
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.692674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 46c1f681-01ac-48a5-955b-4b913bab2e15 · outbound
This paper cites Automated identification of linear viscoelastic constitutive laws with euclid.Mechanics of Materials, 181:104643.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Automated identification of linear viscoelastic constitutive laws with euclid.Mechanics of Materials, 181:104643
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.662464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e4440db0-ca18-476a-9401-e9a82db26c03 · outbound
This paper cites Bayesian-euclid: Discovering hyperelastic material laws with uncertainties.Computer Methods in Applied Mechanics and Engineering, 398:115225.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Bayesian-euclid: Discovering hyperelastic material laws with uncertainties.Computer Methods in Applied Mechanics and Engineering, 398:115225
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.945943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5592b5da-ede9-4ada-9047-3c38a21e017f · outbound
This paper cites Digital image correlation: from displacement measurement to identification of elastic properties–a review.Strain, 42(2):69–80.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Digital image correlation: from displacement measurement to identification of elastic properties–a review.Strain, 42(2):69–80
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.918979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c65aa2c5-6505-4876-b1c1-8a69aaf845e1 · outbound
This paper cites Springer Science & Business Media.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Springer Science & Business Media
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.922193Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e4e471ba-5b8b-4774-9761-c9bbfa5b507c · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:42.915374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9b181812-c1e5-460d-b09d-d4d90a7f00f7 · outbound
This paper cites Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-08-03T01:25:35.057459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a624a8f7-3e7b-44bb-a5da-4a3aadc294c8 · outbound
This paper cites The internal law of a material can be discovered from its boundary.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) The internal law of a material can be discovered from its boundary
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.653853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9583b9de-dd01-485b-be1d-72bc4eceee0c · outbound
This paper cites Solution of inverse problems in elasticity imaging using the adjoint method.Inverse problems, 19(2):297.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Solution of inverse problems in elasticity imaging using the adjoint method.Inverse problems, 19(2):297
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.904747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e3b59726-7e8f-4358-a497-87c729ad8c1b · outbound
This paper cites Overview of identification methods of mechanical parameters based on full-field measurements.Experimental Mechanics, 48(4):381–402.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Overview of identification methods of mechanical parameters based on full-field measurements.Experimental Mechanics, 48(4):381–402
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.171603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 34b0c783-c52b-48d7-be7b-31afd0081e60 · outbound
This paper cites Variation-matching sensitivity-based virtual fields for hyperelastic material model calibration.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Variation-matching sensitivity-based virtual fields for hyperelastic material model calibration
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.674187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1f143157-41d2-4e69-87f9-1cb9de1ff262 · outbound
This paper cites Bayesian approach to micromechanical parameter identification using integrated digital image correlation.International Journal of Solids and Structures, 280:112388.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Bayesian approach to micromechanical parameter identification using integrated digital image correlation.International Journal of Solids and Structures, 280:112388
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.901379Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation eb7c6879-c6de-486e-a8f9-a70c7592dc8b · outbound
This paper cites Calibrating constitutive models with full-field data via physics informed neural networks.Strain, 59(2):e12431.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Calibrating constitutive models with full-field data via physics informed neural networks.Strain, 59(2):e12431
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.180061Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 696de0ca-f5dc-49a3-85de-610d6e373584 · outbound
This paper cites Differentiable hybrid neural modeling for fluid-structure interaction.Journal of Computational Physics, 496:112584.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Differentiable hybrid neural modeling for fluid-structure interaction.Journal of Computational Physics, 496:112584
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.183018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation bbd94dad-eb0a-4c85-9fef-0c436607db87 · outbound
This paper cites Differentiable simulations for pytorch, tensorflow and jax.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Differentiable simulations for pytorch, tensorflow and jax
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.950267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b0df6126-047b-4531-8136-2519ae547d61 · outbound
This paper cites Warp: Differentiable spatial computing for python.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Warp: Differentiable spatial computing for python
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.174240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 195a3a5d-ea88-4641-96af-6f9a7eab0765 · outbound
This paper cites Automatic differentiation in machine learning: a survey.Journal of machine learning research, 18(153):1–43.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Automatic differentiation in machine learning: a survey.Journal of machine learning research, 18(153):1–43
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.142205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3a5c469a-8b25-4a0f-bd25-34ea8ceaadf1 · outbound
This paper cites Jax-fem: A differentiable gpu-accelerated 3d finite element solver for automatic inverse design and mechanistic data science.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Jax-fem: A differentiable gpu-accelerated 3d finite element solver for automatic inverse design and mechanistic data science
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.144960Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 09db152e-74e2-4e41-997c-d085688a7a1c · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 32
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.147454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4092abcb-7a4d-451b-83a4-435a6ce6b3e7 · outbound
This paper cites JAX-SSO: Differentiable Finite Element Analysis Solver for Structural Optimization and Seamless Integration with Neural Networks.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) JAX-SSO: Differentiable Finite Element Analysis Solver for Structural Optimization and Seamless Integration with Neural Networks
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.666329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b9fea400-4132-450d-aaec-46103807ee2a · outbound
This paper cites Efficient gpu-computing simulation platform jax-cpfem for differentiable crystal plasticity finite element method.npj Computational Materials, 11(1):46.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Efficient gpu-computing simulation platform jax-cpfem for differentiable crystal plasticity finite element method.npj Computational Materials, 11(1):46
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.150774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3f087268-d5c0-4c86-b645-2dc7cca6731b · outbound
This paper cites A survey on transfer learning.IEEE Transactions on knowledge and data engineering, 22(10):1345–1359.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) A survey on transfer learning.IEEE Transactions on knowledge and data engineering, 22(10):1345–1359
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.177115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cd013d1a-989b-4ef7-bf11-42f25b75407c · outbound
This paper cites Tissue-scale biomechanical testing of brain tissue for the calibration of nonlinear material models.Current Protocols, 2(4):e381.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Tissue-scale biomechanical testing of brain tissue for the calibration of nonlinear material models.Current Protocols, 2(4):e381
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:42.876122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6311b794-94c7-4d9e-b8e1-9cf4e31b9194 · outbound
This paper cites Correction: Tissue-scale biomechanical testing of brain tissue for the calibration of nonlinear material models.Current Protocols, 2(4):e438– e438.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Correction: Tissue-scale biomechanical testing of brain tissue for the calibration of nonlinear material models.Current Protocols, 2(4):e438– e438
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.139726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 70eb82be-4920-4115-9517-26509a2c49bd · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 38
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.126115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3e43a70c-5c66-4ba0-b5b5-64ceec6a8bcc · outbound
This paper cites Transfer learning of recurrent neural network-based plasticity models.International Journal for Numerical Methods in Engineering, 125(1):e7357.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Transfer learning of recurrent neural network-based plasticity models.International Journal for Numerical Methods in Engineering, 125(1):e7357
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.094302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c91397af-7f16-4485-8782-835b3bb0b914 · outbound
This paper cites A transfer learning enhanced physics-informed neural network for parameter identification in soft materials.Applied Mathematics and Mechanics, 45(10):1685–1704.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) A transfer learning enhanced physics-informed neural network for parameter identification in soft materials.Applied Mathematics and Mechanics, 45(10):1685–1704
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.134228Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b0aad435-6b9b-4d32-afaa-99dabc959cd6 · outbound
This paper cites Convexity conditions and existence theorems in nonlinear elasticity.Archive for rational mechanics and Analysis, 63(4):337–403.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Convexity conditions and existence theorems in nonlinear elasticity.Archive for rational mechanics and Analysis, 63(4):337–403
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.108684Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1ebfad04-c694-4934-a97b-a21e2f1775e7 · outbound
This paper cites On isotropic, frame-invariant, polyconvex strain-energy functions.Quarterly Journal of Mechanics and Applied Mathematics, 56(4):483–491.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) On isotropic, frame-invariant, polyconvex strain-energy functions.Quarterly Journal of Mechanics and Applied Mathematics, 56(4):483–491
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.185819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 28182b4f-ecdb-47fa-9751-3dd90da5ef47 · outbound
This paper cites The exponentiated hencky-logarithmic strain energy.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) The exponentiated hencky-logarithmic strain energy
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.675076Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f12cfc3f-26d1-4f81-89d5-7d1216d48985 · outbound
This paper cites Polyconvex physics- augmented neural network constitutive models in principal stretches.International Journal of Solids and Structures, page 113469.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Polyconvex physics- augmented neural network constitutive models in principal stretches.International Journal of Solids and Structures, page 113469
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.168629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c1bec756-94ca-4a92-8497-c70bb405afc9 · outbound
This paper cites Input convex neural networks.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Input convex neural networks
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.679489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 917c9470-9c2e-43ff-8104-4c0218d56c6b · outbound
This paper cites Polyconvex anisotropic hyperelasticity with neural networks.Journal of the Mechanics and Physics of Solids, 159:104703.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Polyconvex anisotropic hyperelasticity with neural networks.Journal of the Mechanics and Physics of Solids, 159:104703
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:14:21.688465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4a984f62-2d8c-43f4-ad3b-e10a52d1cfa4 · outbound
This paper cites PhD thesis.
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.153429Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a561695f-d22d-48f3-8603-805828ccd4a1 · outbound
This paper cites Modular machine learning- based elastoplasticity: Generalization in the context of limited data.Computer Methods in Applied Mechanics and Engineering, 407:115930.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Modular machine learning- based elastoplasticity: Generalization in the context of limited data.Computer Methods in Applied Mechanics and Engineering, 407:115930
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.162950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 270af796-c9d4-4294-9880-04354cb3d760 · outbound
This paper cites Neural networks meet hyperelasticity: A monotonic approach.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Neural networks meet hyperelasticity: A monotonic approach
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T06:15:56.661724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 11019db2-3a00-45f0-a868-9f64da6c5fc3 · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 50
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.002157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a0cee386-6d3d-447b-bd13-257c8250abb2 · outbound
This paper cites Toward selecting optimal predictive multiscale models.Computer Methods in Applied Mechanics and Engineering, 402:115517.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Toward selecting optimal predictive multiscale models.Computer Methods in Applied Mechanics and Engineering, 402:115517
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.188783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 51cbc468-7ad0-4ae2-a61b-75469d21cfd7 · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 52
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:42.981816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cbb7647d-ec94-4138-9a43-6abb3c8603ed · outbound
This paper cites Hodges, Figgis.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Hodges, Figgis
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.111308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d9f1cb4f-f5de-40c6-a807-0813fd434e8f · outbound
This paper cites A good practices guide for digital image correlation.International Digital Image Correlation Society, 10:1–110.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) A good practices guide for digital image correlation.International Digital Image Correlation Society, 10:1–110
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.118152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 65947070-4733-4a47-97e0-bd48206b4c2f · outbound
This paper cites Assessment of digital image correlation measurement errors: methodology and results.Experimental mechanics, 49(3):353–370.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Assessment of digital image correlation measurement errors: methodology and results.Experimental mechanics, 49(3):353–370
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.102886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f25919d9-a8f9-467a-a20f-0bbe559f5d1c · outbound
This paper cites an unresolved cited work.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Unresolved cited work
Reference 56
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T09:11:43.105600Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0131d76d-e583-4d3a-a1ed-999037fa21b6 · outbound
This paper cites A scalable framework for multi-objective pde-constrained design of building insulation under uncertainty.Computer Methods in Applied Mechanics and Engineering, 419:116628.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) A scalable framework for multi-objective pde-constrained design of building insulation under uncertainty.Computer Methods in Applied Mechanics and Engineering, 419:116628
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.115249Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c9560e27-f56b-469a-812b-65b1b4faa3ad · outbound
This paper cites Springer Science & Business Media.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Springer Science & Business Media
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.123169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6a15fc74-1182-444b-93b5-cb308427a616 · outbound
This paper cites The fenics project version 1.5.Archive of numerical software, 3(100).
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) The fenics project version 1.5.Archive of numerical software, 3(100)
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.137171Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 796b69b3-09d4-442f-a479-8267bb595a1e · outbound
This paper cites Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272.
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T09:11:43.131418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation c5039026-1c75-4a16-ab20-5a69fff1877a · inbound
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data cites this paper.
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T15:04:46.461643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 392d7979-f7c4-417a-b081-1f7d27c8f7e7 · inbound
Towards end-to-end optimization in multimaterial 3D printing cites this paper.
Towards end-to-end optimization in multimaterial 3D printing Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T06:04:18.451578Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 459c2ae9-0160-4326-b44b-a807c9632c2d · inbound
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling cites this paper.
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T10:11:58.982133Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.