Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:21:42.432650Z
Paper Citation Record · LEDGER
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
As of 15 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 2 inbound Pith citation observations for arXiv:2506.10862.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T22:15:33.807145Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
64 of 64 outbound references displayed
- verified exact0
- verified fuzzy58
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation 040c669a-2bd8-443c-9456-3f735d14aec0 · outbound
This paper cites Analysis of a civil aircraft wing transonic shock buffet experiment.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Analysis of a civil aircraft wing transonic shock buffet experiment
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.848556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5e0c2386-ba47-4140-80b7-b6a7b4d87eee · outbound
This paper cites The quiet revolution of numerical weather prediction.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics The quiet revolution of numerical weather prediction
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:37.966665Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a904b473-b199-4bab-9fb8-5fd69e105e79 · outbound
This paper cites Ac- tive generation and magnetic actuation of microrobotic swarms in bio-fluids.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Ac- tive generation and magnetic actuation of microrobotic swarms in bio-fluids.Nat
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.827204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ba6162a7-76f3-4b2c-943c-f3b1942fd88c · outbound
This paper cites Fundamentals of computational fluid dynamics.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Fundamentals of computational fluid dynamics
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.814114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9ccd5c0c-dfc0-4c2d-be02-5fbd44592917 · outbound
This paper cites Physics-informed machine learning.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Physics-informed machine learning.Nat
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.801089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a7eb19fc-5388-45a8-961b-5be152f88c38 · outbound
This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.787869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 709163dd-9d18-4f7e-ab9c-60fcecc6f1bd · outbound
This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:38.400760Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2fdc1b39-64c6-4358-8f2e-e7ad28aa4f12 · outbound
This paper cites PINNacle: A comprehensive benchmark of physics- informed neural networks for solving pdes.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics PINNacle: A comprehensive benchmark of physics- informed neural networks for solving pdes
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.766306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1731afc1-d70f-43c3-9370-36076ed12a57 · outbound
This paper cites Physics-informed learning of governing equations from scarce data.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Physics-informed learning of governing equations from scarce data
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.753206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3a6ea285-4c1f-4f6a-9d91-24881ab602e4 · outbound
This paper cites Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion.Research, 6:0147, 2023.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion.Research, 6:0147, 2023
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:38.565997Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb0e42a5-cbac-479a-bc7f-392e4a58981f · outbound
This paper cites Characterizing possible failure modes in physics-informed neural networks.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Characterizing possible failure modes in physics-informed neural networks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.731397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 90108018-3a9a-4ab9-9af7-edede30082b2 · outbound
This paper cites Can physics-informed neural networks beat the finite element method?IMA J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Can physics-informed neural networks beat the finite element method?IMA J
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.718114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 48b377d8-1e91-4a6c-bac6-1e66a000fec4 · outbound
This paper cites Neural operators for accelerating scientific simulations and design.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Neural operators for accelerating scientific simulations and design.Nat
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.705534Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 609c51e8-1434-4d55-b46c-6fb65c04d066 · outbound
This paper cites Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.693410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5dca042e-a912-44d4-b575-18b5c29985f5 · outbound
This paper cites Learn- ing nonlinear operators in latent spaces for real-time predictions of complex dynamics in phys- ical systems.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Learn- ing nonlinear operators in latent spaces for real-time predictions of complex dynamics in phys- ical systems
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.680723Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0aec8e12-abd4-49c8-b1f0-b40310dd0011 · outbound
This paper cites DeepONet based preconditioning strategies for solving parametric linear systems of equations.SIAM J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics DeepONet based preconditioning strategies for solving parametric linear systems of equations.SIAM J
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.668018Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b0b2517a-b4c4-4857-b7e4-c792211d017a · outbound
This paper cites Factorized Fourier neural operators.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Factorized Fourier neural operators
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.655844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation abe02996-a867-4447-91f9-996b908cc476 · outbound
This paper cites Pre- dictionofturbulentchannelflowusingFourierneuraloperator-basedmachine-learningstrategy.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Pre- dictionofturbulentchannelflowusingFourierneuraloperator-basedmachine-learningstrategy
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.643407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1f0c1864-e175-40fa-b539-04bd4767c04a · outbound
This paper cites Worrall, and Max Welling.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Worrall, and Max Welling
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.631658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 916a8ee6-e84b-4002-ad25-fa1024332ccb · outbound
This paper cites PhyMPGN: Physics-encoded message passing graph network for spatiotemporal PDE systems.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics PhyMPGN: Physics-encoded message passing graph network for spatiotemporal PDE systems
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.618802Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6df24534-bda2-4731-831e-d15d342c1cae · outbound
This paper cites Conservation-informed graph learning for spatiotemporal dynamics prediction.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Conservation-informed graph learning for spatiotemporal dynamics prediction
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.606180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 465d0e0f-6e6c-4bb6-a4fe-bba1d2fc8337 · outbound
This paper cites Scalable Transformer for PDE surrogate mod- eling.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Scalable Transformer for PDE surrogate mod- eling
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.594110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 911920ce-7ff4-4990-a6fc-606cf03f86d9 · outbound
This paper cites A Transformer-based neural operator for large-eddy simulation of turbulence.Phys.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics A Transformer-based neural operator for large-eddy simulation of turbulence.Phys
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.581362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8b7179ee-5c3f-47f8-a7a5-9b351439c21d · outbound
This paper cites Conditional neural field latent diffusion model for generating spatiotemporal turbulence.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Conditional neural field latent diffusion model for generating spatiotemporal turbulence.Nat
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.568757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0f4469a3-9b51-4109-9aeb-257bbeeba08a · outbound
This paper cites Generative learning for forecasting the dynamics of high-dimensional complex systems.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Generative learning for forecasting the dynamics of high-dimensional complex systems.Nat
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.556027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ea821feb-6070-45d4-8ef0-b10c0420c7c5 · outbound
This paper cites Physics-guided, physics-informed, andphysics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics.J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Physics-guided, physics-informed, andphysics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics.J
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.544073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2f553bfb-ccad-45f6-b712-0ea4e99adf34 · outbound
This paper cites Learning the solution operator of parametric partial differential equations with physics-informed DeepONets.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.532001Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 297ba2ee-7360-49fa-82c5-6a8945de272a · outbound
This paper cites Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning.Com- put.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning.Com- put
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.520075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 822bfa71-238b-40d0-8c44-baabdde29eee · outbound
This paper cites Physics-informed neural operator for learning partial differential equations.ACM/JMS J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Physics-informed neural operator for learning partial differential equations.ACM/JMS J
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.508107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 11b23e5e-790e-4b49-ad34-cd3afb4f9086 · outbound
This paper cites Variational physics-informed neural operator (VINO) for solving partial differential equations.Comput.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Variational physics-informed neural operator (VINO) for solving partial differential equations.Comput
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.496294Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1809880f-5d13-451e-85d8-df84ff788ada · outbound
This paper cites Monte Carlo neural PDE solver for learning PDEs via probabilistic representation.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Monte Carlo neural PDE solver for learning PDEs via probabilistic representation
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.484157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 324e796c-9819-4dab-bdec-0b7ddabdce61 · outbound
This paper cites PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network.J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network.J
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.472511Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 502a0fb8-e3ea-4b18-906b-7cf3b0dd4ea1 · outbound
This paper cites Encoding physics to learn reaction–diffusion processes.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Encoding physics to learn reaction–diffusion processes.Nat
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.460504Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8c10f6b7-170a-4495-a727-8768a1500b44 · outbound
This paper cites P2C2Net: PDE-preserved coarse correction network for efficient prediction of spatiotemporal dynamics.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics P2C2Net: PDE-preserved coarse correction network for efficient prediction of spatiotemporal dynamics
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.447897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5ccf79f9-9a26-469d-8f52-517f4f3a41f7 · outbound
This paper cites Multi-resolution partial differ- ential equations preserved learning framework for spatiotemporal dynamics.Commun.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Multi-resolution partial differ- ential equations preserved learning framework for spatiotemporal dynamics.Commun
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.434821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9651b753-ec49-46ca-bbcd-d34094d9a3c5 · outbound
This paper cites MultiPDENet: PDE-embedded learning with multi-time-stepping for accelerated flow simulation.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics MultiPDENet: PDE-embedded learning with multi-time-stepping for accelerated flow simulation
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.421927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d2da4484-1451-402f-9405-570948ccad50 · outbound
This paper cites Machine learning–accelerated computational fluid dynamics.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Machine learning–accelerated computational fluid dynamics
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.408980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 261f3095-f3d5-498b-8314-a739f1a660c9 · outbound
This paper cites A neural PDE solver with temporal stencil modeling.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics A neural PDE solver with temporal stencil modeling
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.394988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b58c2a28-a34f-47b0-a909-a694266e0fb7 · outbound
This paper cites Graph neural PDE solvers with conservation and similarity-equivariance.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Graph neural PDE solvers with conservation and similarity-equivariance
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.381725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2ed05817-c49c-4216-8ce8-1d6f33669bf4 · outbound
This paper cites Learnable-differentiable finite volume solver for acceler- ated simulation of flows.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Learnable-differentiable finite volume solver for acceler- ated simulation of flows
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.369097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8a07e772-dca6-4c60-9e40-61ccf21777b5 · outbound
This paper cites Mesh-informed neural networks for operator learning in finite element spaces.J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Mesh-informed neural networks for operator learning in finite element spaces.J
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.355673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3ce59cd2-3fc6-4306-b51d-14a7ddf0c8e0 · outbound
This paper cites Weak baselines and reporting biases lead to overopti- mism in machine learning for fluid-related partial differential equations.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Weak baselines and reporting biases lead to overopti- mism in machine learning for fluid-related partial differential equations.Nat
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.343325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 053cf0c1-0f35-464e-a627-139534c17f12 · outbound
This paper cites Foundation models for generalist medical artificial intelligence.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Foundation models for generalist medical artificial intelligence
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:40.817004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f77cf4e-b5c6-4f84-be08-622f5379f646 · outbound
This paper cites Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brand- stetter, Patrick Garvan, Maik Riechert, Jonathan A.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brand- stetter, Patrick Garvan, Maik Riechert, Jonathan A
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.321839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 34b160c7-8125-42f7-9ad9-b1365eaa4000 · outbound
This paper cites DPOT: Auto-regressive denoising operator Transformer for large-scale PDE pre-training.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics DPOT: Auto-regressive denoising operator Transformer for large-scale PDE pre-training
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.309293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 822b851c-64d1-4d29-b2a5-7c50f39fc921 · outbound
This paper cites Multiple physics pretraining for spatiotemporal surrogate models.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Multiple physics pretraining for spatiotemporal surrogate models
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.297183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2fa2791f-dac1-41a1-b1e9-e743aa5a174d · outbound
This paper cites Building flexible machine learning models for scientific computing at scale.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Building flexible machine learning models for scientific computing at scale
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.284586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a974821c-3bb6-484c-9f26-2011ea252cf6 · outbound
This paper cites Deep transfer operator learning for partial differential equations under conditional shift.Nat.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Deep transfer operator learning for partial differential equations under conditional shift.Nat
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.272240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 897b203d-159e-47c2-9ce7-a95b8b705ca2 · outbound
This paper cites In-context operator learning with data prompts for differential equation problems.Proc.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics In-context operator learning with data prompts for differential equation problems.Proc
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.259777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ead1380d-cd8d-455e-b8b6-60a4c18105e4 · outbound
This paper cites Learning spatiotemporal dynamics with a pretrained generative model.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Learning spatiotemporal dynamics with a pretrained generative model
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.177921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 75a8f294-cf42-4eed-9371-04a639a81ace · outbound
This paper cites On flame propagation under conditions of stoichiometry.SIAM J.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics On flame propagation under conditions of stoichiometry.SIAM J
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:45.030356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 100bf95a-1741-4fe9-ab75-1a7ed51ef626 · outbound
This paper cites Nonlinear analysis of hydrodynamic instability in laminar flames-II.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Nonlinear analysis of hydrodynamic instability in laminar flames-II
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:44.868971Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d2c8dc99-e22d-4b0e-be17-75ac9d85962f · outbound
This paper cites A vorticity-velocity method for the numerical solution of 3D incompressible flows.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics A vorticity-velocity method for the numerical solution of 3D incompressible flows
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:44.635182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2aace689-182f-4f16-91fa-f2c65e6d8e99 · outbound
This paper cites Navier-Stokes equations on thin 3D domains.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Navier-Stokes equations on thin 3D domains
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:44.452660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 55b6b9ff-fb25-4978-9e6a-874d65e93d5b · outbound
This paper cites From zero to tur- bulence: Generative modeling for 3D flow simulation.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics From zero to tur- bulence: Generative modeling for 3D flow simulation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:44.197241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 1fc82b71-a681-453f-a5d8-eee83b8d78b6 · outbound
This paper cites Fourier neural operator for parametric partial differential equations.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Fourier neural operator for parametric partial differential equations
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:43.910752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d074e47e-d3a0-4626-9638-26fabd4cf8ed · outbound
This paper cites Convolutional neural operators for robust and accurate learning of PDEs.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Convolutional neural operators for robust and accurate learning of PDEs
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:43.683186Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 43cb60ab-63cd-4a38-8aff-34246a7a7414 · outbound
This paper cites Bayesian inverse problems for functions and applications to fluid mechanics.Inverse Probl., 25(11):115008, 2009.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Bayesian inverse problems for functions and applications to fluid mechanics.Inverse Probl., 25(11):115008, 2009
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:43.463360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b81dd624-d6d6-44b5-bdec-dbbaaedff4e2 · outbound
This paper cites DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows.Phy.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows.Phy
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:43.274166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f0aa4a25-236a-4a46-9b46-955f8a694513 · outbound
This paper cites Layer Normalization.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Layer Normalization
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:41.945053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7463a73f-253f-4832-b195-7ec5b1d7aed8 · outbound
This paper cites Bridging traditional and machine learning-based algorithms for solving PDEs: The random feature method.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Bridging traditional and machine learning-based algorithms for solving PDEs: The random feature method
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:43.015194Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 9915757d-6d38-47c2-b30d-8c6d5dd12d3d · outbound
This paper cites Distilling the Knowledge in a Neural Network.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Distilling the Knowledge in a Neural Network
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:21:42.186715Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3110c80-0ee8-4a39-b89d-b91441e38981 · outbound
This paper cites PyTorch: An imperative style, high-performance deep learning library.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics PyTorch: An imperative style, high-performance deep learning library
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:42.833794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 109194e3-cd50-4622-9ce3-d6e9ba04432a · outbound
This paper cites Adam: A method for stochastic optimization.
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics Adam: A method for stochastic optimization
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T04:21:42.655342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Pith citing papers
Observation 74478432-226b-47a0-9876-b47b87b86ec0 · inbound
Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence cites this paper.
Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T04:00:13.430167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation c81cf41d-d3eb-43c3-835f-eb8e1598559b · inbound
NPSolver: Neural Poisson Solver with Iterative Physics Supervision cites this paper.
NPSolver: Neural Poisson Solver with Iterative Physics Supervision OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T22:24:00.821301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.