Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T09:18:45.582304Z
Paper Citation Record · LEDGER
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark
As of 19 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2607.18294.
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 2026-08-19T06:32:44.657259+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
7 of 7 outbound references displayed
- verified exact1
- verified fuzzy0
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7b1b7826-6eb3-4493-b1a2-bd3844c81f10 · outbound
This paper cites Automotive Crash Dynamics Modeling Accelerated with Machine Learning,.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Automotive Crash Dynamics Modeling Accelerated with Machine Learning,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:45.083176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e02a8460-dffb-42a9-aa37-d711fbd80146 · outbound
This paper cites High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:45.158235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25a24b85-9932-471b-a9c8-bfc0c08bba7c · outbound
This paper cites GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:45.235148Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18dddca9-4303-42d6-89de-269151a05fc7 · outbound
This paper cites Dropout: A Simple Way to Prevent Neural Networks from Overfitting,.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Dropout: A Simple Way to Prevent Neural Networks from Overfitting,
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:45.340284Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8015632c-1fc2-4a4e-b975-83a3a6f8d5f8 · outbound
This paper cites an unresolved cited work.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Unresolved cited work
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:44.962251Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ed1613b-8745-4b57-befb-6c541f13a9f6 · outbound
This paper cites Deep Ensembles: A Loss Landscape Perspective.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Deep Ensembles: A Loss Landscape Perspective
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:18:45.469539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5b5e976-7358-412a-91b8-dc45b3288ceb · outbound
This paper cites Uncertainty Quantification in Machine Learning Using an Ensemble Approach with Gaussian Process Regression,.
Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Uncertainty Quantification in Machine Learning Using an Ensemble Approach with Gaussian Process Regression,
Reference 16
- Resolution
- verified exact
- doi, observed 2026-08-02T09:23:26.840407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.