Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T13:48:14.532529Z
Paper Citation Record · LEDGER
Flow marching for a generative PDE foundation model
As of 4 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2509.18611.
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 2026-07-31T23:47:27.891346Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
65 of 65 outbound references displayed
- verified exact43
- verified fuzzy17
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch4
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 42ae5851-8db3-4efb-80f1-58d3165d1517 · outbound
This paper cites write newline.
Flow marching for a generative PDE foundation model write newline
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.813805Z
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 14f1ed87-db78-43d5-a662-5c375e49870f · outbound
This paper cites @esa (Ref.
Flow marching for a generative PDE foundation model @esa (Ref
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.817280Z
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 d872f2cf-9014-454a-80cf-0f32a79f0c0c · outbound
This paper cites an unresolved cited work.
Flow marching for a generative PDE foundation model Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T13:51:26.780935Z
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 8708c14a-b6aa-421b-b4f1-212b8583324c · outbound
This paper cites ϟ ` :QYϛV!NU xZ͒K KM.
Flow marching for a generative PDE foundation model ϟ ` :QYϛV!NU xZ͒K KM
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.785077Z
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 0d40fea3-8fe5-4732-a1dd-bf9a4ebf10d4 · outbound
This paper cites Universal physics transformers: A framework for efficiently scaling neural operators.
Flow marching for a generative PDE foundation model Universal physics transformers: A framework for efficiently scaling neural operators
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.788613Z
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 9490dd99-a5c4-49cb-bdba-19d473376aba · outbound
This paper cites Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation.
Flow marching for a generative PDE foundation model Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.479459Z
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 501fd419-0255-4bdf-b76a-56566785b213 · outbound
This paper cites Neural operators for accelerating scientific simulations and design.
Flow marching for a generative PDE foundation model Neural operators for accelerating scientific simulations and design
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.777583Z
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 49d78ffb-0188-4ae6-aef0-eff9845d32af · outbound
This paper cites Nonlinear ensemble filtering with diffusion models: Application to the surface quasi-geostrophic dynamics.
Flow marching for a generative PDE foundation model Nonlinear ensemble filtering with diffusion models: Application to the surface quasi-geostrophic dynamics
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.616475Z
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 90d9080d-0350-40ab-a0d7-cbfdec7a79ad · outbound
This paper cites A score-based filter for nonlinear data assimilation.
Flow marching for a generative PDE foundation model A score-based filter for nonlinear data assimilation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.773951Z
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 b5db45a3-c998-42b7-84fa-8a8bbe6cdd4c · outbound
This paper cites Physics-Informed Diffusion Models.
Flow marching for a generative PDE foundation model Physics-Informed Diffusion Models
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.655084Z
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 58ccd5c4-cb7b-4926-8a94-48973c606e57 · outbound
This paper cites DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting.
Flow marching for a generative PDE foundation model DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
Reference 11
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T13:51:25.463439Z
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 64f1a78b-f778-4a86-a66c-daf4971212ea · outbound
This paper cites Vicon: Vision in-context operator networks for multi-physics fluid dynamics prediction.
Flow marching for a generative PDE foundation model Vicon: Vision in-context operator networks for multi-physics fluid dynamics prediction
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.441389Z
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 8664bf2e-1593-4f1c-ae2a-286d05010159 · outbound
This paper cites Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion.
Flow marching for a generative PDE foundation model Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Reference 13
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T13:51:25.524567Z
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 4cc88b7d-e04f-4363-8f8e-7404c95e37b7 · outbound
This paper cites GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty.
Flow marching for a generative PDE foundation model GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.474305Z
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 c8232ee5-e51e-4d74-a13e-f8adcf9eb3b4 · outbound
This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.
Flow marching for a generative PDE foundation model Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.446334Z
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 94d84c74-113d-47b9-857d-957ce6ac8768 · outbound
This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.
Flow marching for a generative PDE foundation model FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.595404Z
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 67865dd0-faea-4cb0-822d-9870636fd62e · outbound
This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.
Flow marching for a generative PDE foundation model Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.502855Z
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 783eed5c-c00b-4f22-9e63-fa92f743c528 · outbound
This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.
Flow marching for a generative PDE foundation model Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.770410Z
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 14ed74e2-1c78-4587-8266-cd96dfc9d28c · outbound
This paper cites Auto-Regressive Moving Diffusion Models for Time Series Forecasting.
Flow marching for a generative PDE foundation model Auto-Regressive Moving Diffusion Models for Time Series Forecasting
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.665219Z
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 9bc497ef-ca4b-4856-b249-1f3b39a4cda1 · outbound
This paper cites Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing.
Flow marching for a generative PDE foundation model Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.606866Z
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 093802e2-5b9e-4c46-95cd-8bb134fcd597 · outbound
This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.
Flow marching for a generative PDE foundation model Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.581096Z
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 a76ca4bf-c885-4701-ab4f-dcb157ef389b · outbound
This paper cites Learnings from Scaling Visual Tokenizers for Reconstruction and Generation.
Flow marching for a generative PDE foundation model Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.457933Z
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 c4685dd2-b817-4322-8a7b-4d41bf5a6745 · outbound
This paper cites DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.
Flow marching for a generative PDE foundation model DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.491492Z
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 f7f1769f-8f63-4290-af35-fe64a1ef796f · outbound
This paper cites Ho, J., Jain, A., and Abbeel, P.
Flow marching for a generative PDE foundation model Ho, J., Jain, A., and Abbeel, P
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.643237Z
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 55bd3f88-1254-4004-8711-f9fb280f03af · outbound
This paper cites Video Diffusion Models.
Flow marching for a generative PDE foundation model Video Diffusion Models
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.649591Z
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 708e7477-a669-4c1b-bc22-33a557963b0e · outbound
This paper cites DiffusionPDE: Generative PDE-Solving Under Partial Observation.
Flow marching for a generative PDE foundation model DiffusionPDE: Generative PDE-Solving Under Partial Observation
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.611962Z
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 ba5f92c1-72e2-4b84-bedc-659cd488f886 · outbound
This paper cites Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion.
Flow marching for a generative PDE foundation model Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.513569Z
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 83f2817f-cd62-411c-92e4-934d9e663b21 · outbound
This paper cites ACC-UNet: A Completely Convolutional UNet model for the 2020s.
Flow marching for a generative PDE foundation model ACC-UNet: A Completely Convolutional UNet model for the 2020s
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.530539Z
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 21b48cd7-2cb8-4486-b047-0a941f03777c · outbound
This paper cites Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models.
Flow marching for a generative PDE foundation model Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.660585Z
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 446c8ca3-9013-4f3c-a1fe-17c3df22f85e · outbound
This paper cites Pyramidal flow matching for efficient video generative modeling.arXiv preprint arXiv:2410.05954.
Flow marching for a generative PDE foundation model Pyramidal flow matching for efficient video generative modeling.arXiv preprint arXiv:2410.05954
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.601219Z
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 2d622b43-4309-40ad-b7d3-f0e35ce0b5c0 · outbound
This paper cites Neural operator: Learning maps between function spaces with applications to pdes.
Flow marching for a generative PDE foundation model Neural operator: Learning maps between function spaces with applications to pdes
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.759435Z
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 7376f3e1-3284-4989-8410-eecc690d3a61 · outbound
This paper cites Learning nonlinear reduced models from data with operator inference.
Flow marching for a generative PDE foundation model Learning nonlinear reduced models from data with operator inference
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.763004Z
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 15e6e14f-69f3-4c78-9ea6-b34d68c20d41 · outbound
This paper cites Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers.
Flow marching for a generative PDE foundation model Repa-e: Unlocking vae for end-to-end tuning with latent diffusion transformers
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.557408Z
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 cc449060-aab3-451e-8e3c-b9a83f5672a5 · outbound
This paper cites Generative emulation of weather forecast ensembles with diffusion models.
Flow marching for a generative PDE foundation model Generative emulation of weather forecast ensembles with diffusion models
Reference 34
- Resolution
- verified exact
- doi, observed 2026-05-18T13:51:25.344117Z
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 c0a3c2c4-887b-4b17-9072-a34171d3e748 · outbound
This paper cites Transformer for Partial Differential Equations' Operator Learning.
Flow marching for a generative PDE foundation model Transformer for Partial Differential Equations' Operator Learning
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.535307Z
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 a22feddb-fc6c-4168-ba76-9f986a1cd166 · outbound
This paper cites Generative Latent Neural PDE Solver using Flow Matching.
Flow marching for a generative PDE foundation model Generative Latent Neural PDE Solver using Flow Matching
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.540446Z
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 ba66ae8f-7e2e-45a9-b944-4d471645f7a1 · outbound
This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.
Flow marching for a generative PDE foundation model Fourier Neural Operator for Parametric Partial Differential Equations
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.451982Z
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 d4db6ff4-ce1a-4aff-87d7-a2bdf05ce905 · outbound
This paper cites Physics-informed neural operator for learning partial differential equations.
Flow marching for a generative PDE foundation model Physics-informed neural operator for learning partial differential equations
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.807668Z
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 f13535c4-b1a7-4339-a48d-729bd0bf7afd · outbound
This paper cites Flow Matching for Generative Modeling.
Flow marching for a generative PDE foundation model Flow Matching for Generative Modeling
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.552348Z
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 4d3b7f73-1093-4d5c-b3b7-4dfb1a025aef · outbound
This paper cites Flow-GRPO: Training Flow Matching Models via Online RL.
Flow marching for a generative PDE foundation model Flow-GRPO: Training Flow Matching Models via Online RL
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.562414Z
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 e24a2e19-f0ee-4007-8e70-0591b8450345 · outbound
This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.
Flow marching for a generative PDE foundation model Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.590708Z
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 62bda610-a10a-43b9-81b3-1e146d838772 · outbound
This paper cites PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics.
Flow marching for a generative PDE foundation model PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.630701Z
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 88247227-2ab9-4951-b591-af3385dfb5b6 · outbound
This paper cites Physics informed token transformer for solving partial differential equations.
Flow marching for a generative PDE foundation model Physics informed token transformer for solving partial differential equations
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.766856Z
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 dca72ce0-ba49-40ba-8215-89fb02198940 · outbound
This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.
Flow marching for a generative PDE foundation model Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.755528Z
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 d8cc6f4e-e343-4de9-8dbb-618ed047e943 · outbound
This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.
Flow marching for a generative PDE foundation model SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.634942Z
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 b1f212b1-0a2a-44a8-af4f-9ad4eda950e8 · outbound
This paper cites Multiple Physics Pretraining for Physical Surrogate Models.
Flow marching for a generative PDE foundation model Multiple Physics Pretraining for Physical Surrogate Models
Reference 46
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.497762Z
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 bf194db3-e7bb-4de6-8390-de23267c6976 · outbound
This paper cites The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning.
Flow marching for a generative PDE foundation model The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.572303Z
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 52179522-052a-4cc6-b76f-d229dd364c0a · outbound
This paper cites Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling.
Flow marching for a generative PDE foundation model Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.621333Z
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 0571b66a-5d88-4b6d-9748-2fb047c4a79e · outbound
This paper cites On Calibrating Diffusion Probabilistic Models.
Flow marching for a generative PDE foundation model On Calibrating Diffusion Probabilistic Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.518825Z
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 5c92964f-c6c3-43e3-a803-e263b19db0fb · outbound
This paper cites Scalable Diffusion Models with Transformers.
Flow marching for a generative PDE foundation model Scalable Diffusion Models with Transformers
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.585536Z
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 74949ddb-b160-44d0-9461-a20172d2a85c · outbound
This paper cites GenCast: Diffusion-based ensemble forecasting for medium-range weather.
Flow marching for a generative PDE foundation model GenCast: Diffusion-based ensemble forecasting for medium-range weather
Reference 51
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T13:51:25.625901Z
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 721911a3-78db-4067-b5f8-28e71db48c96 · outbound
This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.
Flow marching for a generative PDE foundation model High-Resolution Image Synthesis with Latent Diffusion Models
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.468194Z
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 25d37f88-68f5-4292-a042-076529afe40e · outbound
This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.
Flow marching for a generative PDE foundation model U-Net: Convolutional Networks for Biomedical Image Segmentation
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.576340Z
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 7f873b49-c50d-422d-a19f-b4270c657b13 · outbound
This paper cites Turbulent flow data as pytorch tensors for ml: Kolmogorov flow at re=222, and kelvin-helmholtz instability.
Flow marching for a generative PDE foundation model Turbulent flow data as pytorch tensors for ml: Kolmogorov flow at re=222, and kelvin-helmholtz instability
Reference 54
- Resolution
- verified exact
- doi, observed 2026-05-18T13:51:25.348333Z
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 6df1de84-2412-4633-a4eb-7a933b820d55 · outbound
This paper cites Towards a foundation model for partial differential equations: Multioperator learning and extrapolation.
Flow marching for a generative PDE foundation model Towards a foundation model for partial differential equations: Multioperator learning and extrapolation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.810543Z
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 ad6de6ae-19ec-465a-be0a-aba741fde0d5 · outbound
This paper cites PDEBENCH: An Extensive Benchmark for Scientific Machine Learning.
Flow marching for a generative PDE foundation model PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.485863Z
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 e85d3d44-fbaf-468c-9262-137cdd5948dd · outbound
This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.
Flow marching for a generative PDE foundation model Improving and generalizing flow-based generative models with minibatch optimal transport
Reference 57
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.545967Z
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 5147cf39-d010-4236-8421-a8e9397c5fa1 · outbound
This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.
Flow marching for a generative PDE foundation model Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:51:25.639111Z
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 69b96cb3-7354-4d38-99ff-ac3015134a4a · outbound
This paper cites Attention is all you need.
Flow marching for a generative PDE foundation model Attention is all you need
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.804502Z
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 52c4445e-de8b-4763-9bd6-30acb447fecc · outbound
This paper cites Physics-guided training of GAN to improve accuracy in airfoil design synthesis.
Flow marching for a generative PDE foundation model Physics-guided training of GAN to improve accuracy in airfoil design synthesis
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.508363Z
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 e25282a3-0b28-4e3b-acd1-7152b28645cf · outbound
This paper cites Progressive autoregressive video diffusion models.
Flow marching for a generative PDE foundation model Progressive autoregressive video diffusion models
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.796046Z
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 9462ec76-085d-4821-ae9a-2017861959f2 · outbound
This paper cites In-context operator learning with data prompts for differential equation problems.
Flow marching for a generative PDE foundation model In-context operator learning with data prompts for differential equation problems
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.801406Z
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 46f534cb-4d66-409d-9c6e-d1e61a16f874 · outbound
This paper cites PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations.
Flow marching for a generative PDE foundation model PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:51:25.567782Z
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 456aa972-9775-4d3a-ba34-8d6c1778dc7c · outbound
This paper cites MatterGen: a generative model for inorganic materials design.
Flow marching for a generative PDE foundation model MatterGen: a generative model for inorganic materials design
Reference 64
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T13:51:25.669998Z
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 eb7c5905-5b73-432f-800e-d6d49d382f2b · outbound
This paper cites Upscale-a-video: Temporal-consistent diffusion model for real-world video super-resolution.
Flow marching for a generative PDE foundation model Upscale-a-video: Temporal-consistent diffusion model for real-world video super-resolution
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T13:51:26.792263Z
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 4ff7826f-ff74-4b1e-9e6e-c5363a52a9d2 · inbound
Neural operator discovery from heterogeneous trajectories cites this paper.
Neural operator discovery from heterogeneous trajectories Flow marching for a generative PDE foundation model
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T23:47:27.891346Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.