Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:51:24.309608Z
Paper Citation Record · LEDGER
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
As of 14 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2506.22453.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-08T01:35:01.859305Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T23:06:21.162838Z
Reference resolution
23 of 23 outbound references displayed
- verified exact0
- verified fuzzy16
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 223ff43d-9c59-48f6-ad8b-73d7ab39432c · outbound
This paper cites A., Molecular Gas Dynamics and the Direct Simulation of Gas Flows, Oxford Univ.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks A., Molecular Gas Dynamics and the Direct Simulation of Gas Flows, Oxford Univ
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.676883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 25705a11-73fd-46d7-90d9-95d1bed9b069 · outbound
This paper cites A., The DSMC Method, 2013.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks A., The DSMC Method, 2013
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.661799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7f2d07cd-cda7-4db9-88d9-545ed6c864e4 · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.647368Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3367b2eb-c2d0-48ca-9ad3-4f4c58e049ad · outbound
This paper cites Variable Soft Sphere molecular model for accurate rarefied gas flow simulation,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Variable Soft Sphere molecular model for accurate rarefied gas flow simulation,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.632642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e6031514-5c5f-4105-8886-83925e230621 · outbound
This paper cites Collision sampling schemes for the Boltzmann equation and DSMC,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Collision sampling schemes for the Boltzmann equation and DSMC,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.617826Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 47217ce3-9f62-4e00-aa8e-a00857dab12a · outbound
This paper cites Goshayeshi, E.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Goshayeshi, E
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.603339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9006cd4b-9da2-4fcf-be41-e03f27a7bfcc · outbound
This paper cites Shoja-sani, E.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Shoja-sani, E
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.589057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c9d3df3d-2deb-4d73-b092-25fab20d3a8b · outbound
This paper cites Javani, E.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Javani, E
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.574580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation fc16440e-896b-4bf9-9453-b371fd11e1de · outbound
This paper cites Neural -network acceleration of the Boltzmann collision integral,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Neural -network acceleration of the Boltzmann collision integral,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.559454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation fda45c4e-896e-40e6-b673-5f48ead6fb38 · outbound
This paper cites A neural -network-based collision operator for DSMC,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks A neural -network-based collision operator for DSMC,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.543908Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7194d865-9228-4524-8d8f-333d92abd5ab · outbound
This paper cites Hybrid lattice -Boltzmann/NN-BGK solver for rarefied gas dynamics,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Hybrid lattice -Boltzmann/NN-BGK solver for rarefied gas dynamics,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.528752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation da3df2b8-7d7f-4450-8ba7-ca358f0969f2 · outbound
This paper cites Physics -informed neural networks for the spatially homogeneous Boltzmann equation,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Physics -informed neural networks for the spatially homogeneous Boltzmann equation,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.513770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c318e435-96dd-42a2-be12-42987fc6ec88 · outbound
This paper cites Deep-learning surrogate coupling for DSMC–CFD microflow simulations,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Deep-learning surrogate coupling for DSMC–CFD microflow simulations,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.498627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a3183641-5729-472f-ae63-98a303dcd517 · outbound
This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.483391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 0ed98ef7-5fe3-4b5a-833e-8abfc5a5d534 · outbound
This paper cites A Deep Domain Decomposition Method Based on Fourier Features (F -D3M),.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks A Deep Domain Decomposition Method Based on Fourier Features (F -D3M),
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.468088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 704d9675-eab4-4028-ac1b-db7925e3394e · outbound
This paper cites Deep Ritz Method with Fourier Feature Mapping: A Deep Learning Approach for Solving Variational Models of Microstructure,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Deep Ritz Method with Fourier Feature Mapping: A Deep Learning Approach for Solving Variational Models of Microstructure,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.452817Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 59bae8d7-54aa-42c0-b8fc-1b36b364b7cc · outbound
This paper cites SASNet: Spatially-Adaptive Sinusoidal Neural Networks,.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks SASNet: Spatially-Adaptive Sinusoidal Neural Networks,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T00:51:24.438223Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bdccfb97-00f1-4c1d-9f0b-31c1c5de83bc · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.421462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6e1c656c-41ac-4fe9-9d94-56e9425999a3 · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.404806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 2136279a-aae3-4b8c-8eb2-bc91e0485d83 · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.390447Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation f82136c5-e4bc-474a-938b-4ba15dfd2fa4 · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.375872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4435c975-06c3-4ab5-a2a2-22f7f527424c · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.360704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e4c98153-ba34-4e44-97fc-9f858d816a50 · outbound
This paper cites an unresolved cited work.
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks Unresolved cited work
Reference 24
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T00:51:24.345172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
Observation 94ec8a15-c2c8-4b26-9358-0c15f47d40a3 · inbound
Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling cites this paper.
Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:06:21.166109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.