Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2505.07801.
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, observed 2026-08-06T16:38:24.930442Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T23:19:03.764826Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c7f2fcee-9bd5-45ce-84a1-bef5347f9131 · inbound
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling cites this paper.
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:38:24.930442Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd2cea0b-7fe1-44f3-bba9-5e3cbd1cad0b · inbound
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 cites this paper.
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 9b181812-c1e5-460d-b09d-d4d90a7f00f7 · inbound
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU) cites this paper.
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 2026-08-11T06:34:44.6726+00:00.
Observation 3ac09a9a-d7c8-44e3-aa78-2972c96cebd4 · inbound
JAX-FEM-ANISO: Differentiable GPU-Accelerated Finite Element Framework for Inverse Identification of Finite-Strain Anisotropic Plasticity cites this paper.
JAX-FEM-ANISO: Differentiable GPU-Accelerated Finite Element Framework for Inverse Identification of Finite-Strain Anisotropic Plasticity Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Reference 49
- 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 2026-08-11T06:34:44.6726+00:00.
Observation 3b825c44-1936-483a-b1c7-d18242258848 · inbound
JAX-FEM-ANISO: Differentiable GPU-Accelerated Finite Element Framework for Inverse Identification of Finite-Strain Anisotropic Plasticity cites this paper.
JAX-FEM-ANISO: Differentiable GPU-Accelerated Finite Element Framework for Inverse Identification of Finite-Strain Anisotropic Plasticity Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T17:49:01.047724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.